Resolving review comments.

Author: MarkDaoust

samples/core/guide/autograph_control_flow.ipynb

@@ -10,6 +10,7 @@
       "collapsed_sections": [
         "Jxv6goXm7oGF"
       ],
+      "toc_visible": true,
       "include_colab_link": true
     },
     "kernelspec": {
@@ -89,6 +90,16 @@
         "[AutoGraph](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/README.md) helps you write complicated graph code using just plain Python -- behind the scenes, AutoGraph automatically transforms your code into the equivalent TF graph code. We support a large chunk of the Python language, which is growing. [Please see this document for what we currently support, and what we're working on](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/LIMITATIONS.md)."
       ]
     },
+    {
+      "metadata": {
+        "id": "n4EKOpw9mObL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Setup"
+      ]
+    },
     {
       "metadata": {
         "id": "mT7meGqrZTz9",
@@ -103,12 +114,26 @@
         "\n",
         "import tensorflow as tf\n",
         "from tensorflow.contrib import autograph\n",
+        "tf.enable_eager_execution()\n",
         "\n",
         "import matplotlib.pyplot as plt"
       ],
       "execution_count": 0,
       "outputs": []
     },
+    {
+      "metadata": {
+        "id": "ohbSnA79mcJV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Automatically converting control flow\n",
+        "\n",
+        "AutoGraph can convert a large chunk of the Python language into equivalent graph-construction code, and we're adding new supported language features all the time. In this section, we'll give you a taste of some of the functionality in AutoGraph.\n",
+        "AutoGraph will automatically convert most Python control flow statements into their correct graph equivalent.  "
+      ]
+    },
     {
       "metadata": {
         "id": "Ry0TlspBZVvf",
@@ -144,7 +169,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "Into graph-compatible functions like this:"
+        "Into graph-building functions like this:"
       ]
     },
     {
@@ -167,7 +192,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "You can take code written for eager execution and run it in graph mode. You get the same results, but with all the benfits of graphs:"
+        "You can take code written for eager execution and run it in a `tf.Graph`. You get the same results, but with all the benfits of graphs:"
       ]
     },
     {
@@ -178,7 +203,7 @@
       },
       "cell_type": "code",
       "source": [
-        "print('Original value: %2.2f' % g(9.0))  "
+        "print('Eager results: %2.2f, %2.2f' % (g(tf.constant(9.0)), g(tf.constant(-9.0))))"
       ],
       "execution_count": 0,
       "outputs": []
@@ -206,9 +231,10 @@
         "with tf.Graph().as_default():  \n",
         "  # The result works like a regular op: takes tensors in, returns tensors.\n",
         "  # You can inspect the graph using tf.get_default_graph().as_graph_def()\n",
-        "  g_ops = tf_g(tf.constant(9.0))\n",
+        "  g_out1 = tf_g(tf.constant( 9.0))\n",
+        "  g_out2 = tf_g(tf.constant(-9.0))\n",
         "  with tf.Session() as sess:\n",
-        "    print('Autograph value: %2.2f\\n' % sess.run(g_ops))  "
+        "    print('Graph results: %2.2f, %2.2f\\n' % (sess.run(g_out1), sess.run(g_out2)))"
       ],
       "execution_count": 0,
       "outputs": []
@@ -220,12 +246,6 @@
       },
       "cell_type": "markdown",
       "source": [
-        "## Automatically converting control flow\n",
-        "\n",
-        "AutoGraph can convert a large chunk of the Python language into equivalent graph-construction code, and we're adding new supported language features all the time. In this section, we'll give you a taste of some of the functionality in AutoGraph.\n",
-        "AutoGraph will automatically convert most Python control flow statements into their correct graph equivalent.  \n",
-        "  \n",
-        "\n",
         "We support common statements like `while`, `for`, `if`, `break`, `return` and more. You can even nest them as much as you like. Imagine trying to write the graph version of this code by hand:\n"
       ]
     },
@@ -246,13 +266,13 @@
         "    s += c\n",
         "  return s\n",
         "\n",
-        "print('Original value: %d' % f([10,12,15,20]))\n",
+        "print('Eager result: %d' % f(tf.constant([10,12,15,20])))\n",
         "\n",
         "tf_f = autograph.to_graph(f)\n",
         "\n",
         "with tf.Graph().as_default():  \n",
         "  with tf.Session():\n",
-        "    print('Graph value: %d\\n\\n' % tf_f(tf.constant([10,12,15,20])).eval())"
+        "    print('Graph result: %d\\n\\n' % tf_f(tf.constant([10,12,15,20])).eval())"
       ],
       "execution_count": 0,
       "outputs": []
@@ -318,20 +338,25 @@
         "with tf.Graph().as_default():  \n",
         "  # The result works like a regular op: takes tensors in, returns tensors.\n",
         "  # You can inspect the graph using tf.get_default_graph().as_graph_def()\n",
-        "  input = tf.placeholder(tf.int32)\n",
-        "  result = fizzbuzz(input)\n",
+        "  num = tf.placeholder(tf.int32)\n",
+        "  result = fizzbuzz(num)\n",
         "  with tf.Session() as sess:\n",
-        "    sess.run(result, feed_dict={input:10})    \n",
-        "    sess.run(result, feed_dict={input:11})    \n",
-        "    sess.run(result, feed_dict={input:12})    \n",
-        "    sess.run(result, feed_dict={input:13})    \n",
-        "    sess.run(result, feed_dict={input:14}) \n",
-        "    sess.run(result, feed_dict={input:15}) \n",
-        "    "
+        "    for n in range(10,16):\n",
+        "      sess.run(result, feed_dict={num:n})   "
       ],
       "execution_count": 0,
       "outputs": []
     },
+    {
+      "metadata": {
+        "id": "raExYNNZgibS",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Examples"
+      ]
+    },
     {
       "metadata": {
         "id": "-pkEH6OecW7h",
@@ -352,18 +377,17 @@
       },
       "cell_type": "code",
       "source": [
+        "@autograph.convert()\n",
         "def f(x):\n",
         "  assert x != 0, 'Do not pass zero!'\n",
         "  return x * x\n",
         "\n",
-        "tf_f = autograph.to_graph(f)\n",
-        "\n",
         "with tf.Graph().as_default():  \n",
         "  with tf.Session():\n",
         "    try:\n",
         "      print(tf_f(tf.constant(0)).eval())\n",
         "    except tf.errors.InvalidArgumentError as e:\n",
-        "      print('Got error message:\\n%s' % e.message)"
+        "      print('Got error message:\\n    %s' % e.message)"
       ],
       "execution_count": 0,
       "outputs": []
@@ -412,7 +436,7 @@
       "source": [
         "### Lists\n",
         "\n",
-        "Appending to lists in loops also works (we create a `TensorArray` for you behind the scenes)"
+        "Appending to lists in loops also works (we create tensor list ops for you behind the scenes)."
       ]
     },
     {
@@ -423,6 +447,7 @@
       },
       "cell_type": "code",
       "source": [
+        "@autograph.convert()\n",
         "def f(n):\n",
         "  z = []\n",
         "  # We ask you to tell us the element dtype of the list\n",
@@ -433,11 +458,11 @@
         "  # (this is just like np.stack)\n",
         "  return autograph.stack(z) \n",
         "\n",
-        "tf_f = autograph.to_graph(f)\n",
+        "#tf_f = autograph.to_graph(f)\n",
         "\n",
         "with tf.Graph().as_default():  \n",
         "  with tf.Session():\n",
-        "    print(tf_f(tf.constant(3)).eval())"
+        "    print(f(tf.constant(3)).eval())"
       ],
       "execution_count": 0,
       "outputs": []
@@ -515,7 +540,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "### Break from loop"
+        "### Break"
       ]
     },
     {
@@ -765,6 +790,19 @@
       ],
       "execution_count": 0,
       "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ZpEIfs5jn6jw",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
     }
   ]
 }