Use meaningful function names

MarkDaoust · MarkDaoust · commit 71b64ccb9273 · 2018-07-16T20:53:07.000-07:00
diff --git a/samples/core/guide/autograph.ipynb b/samples/core/guide/autograph.ipynb
@@ -3,21 +3,32 @@
   "nbformat_minor": 0,
   "metadata": {
     "colab": {
-      "name": "autograph.ipynb",
+      "name": "Copy of Copy of Copy of autograph.ipynb",
       "version": "0.3.2",
       "provenance": [],
       "private_outputs": true,
       "collapsed_sections": [
         "Jxv6goXm7oGF"
       ],
-      "toc_visible": true
+      "toc_visible": true,
+      "include_colab_link": true
     },
     "kernelspec": {
       "name": "python3",
       "display_name": "Python 3"
     }
   },
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "[View in Colaboratory](https://colab.research.google.com/github/MarkDaoust/models/blob/autograph/samples/core/guide/autograph.ipynb)"
+      ]
+    },
     {
       "metadata": {
         "id": "Jxv6goXm7oGF",
@@ -199,7 +210,7 @@
       },
       "cell_type": "code",
       "source": [
-        "def g(x):\n",
+        "def square_if_positive(x):\n",
         "  if x > 0:\n",
         "    x = x * x\n",
         "  else:\n",
@@ -227,7 +238,7 @@
       },
       "cell_type": "code",
       "source": [
-        "print(autograph.to_code(g))"
+        "print(autograph.to_code(square_if_positive))"
       ],
       "execution_count": 0,
       "outputs": []
@@ -250,7 +261,8 @@
       },
       "cell_type": "code",
       "source": [
-        "print('Eager results: %2.2f, %2.2f' % (g(tf.constant(9.0)), g(tf.constant(-9.0))))"
+        "print('Eager results: %2.2f, %2.2f' % (square_if_positive(tf.constant(9.0)), \n",
+        "                                       square_if_positive(tf.constant(-9.0))))"
       ],
       "execution_count": 0,
       "outputs": []
@@ -273,13 +285,13 @@
       },
       "cell_type": "code",
       "source": [
-        "tf_g = autograph.to_graph(g)\n",
+        "tf_square_if_positive = autograph.to_graph(square_if_positive)\n",
         "\n",
         "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_out1 = tf_g(tf.constant( 9.0))\n",
-        "  g_out2 = tf_g(tf.constant(-9.0))\n",
+        "  g_out1 = tf_square_if_positive(tf.constant( 9.0))\n",
+        "  g_out2 = tf_square_if_positive(tf.constant(-9.0))\n",
         "  with tf.Session() as sess:\n",
         "    print('Graph results: %2.2f, %2.2f\\n' % (sess.run(g_out1), sess.run(g_out2)))"
       ],
@@ -305,21 +317,21 @@
       "cell_type": "code",
       "source": [
         "# Continue in a loop\n",
-        "def f(l):\n",
+        "def sum_even(items):\n",
         "  s = 0\n",
-        "  for c in l:\n",
+        "  for c in items:\n",
         "    if c % 2 > 0:\n",
         "      continue\n",
         "    s += c\n",
         "  return s\n",
         "\n",
-        "print('Eager result: %d' % f(tf.constant([10,12,15,20])))\n",
+        "print('Eager result: %d' % sum_even(tf.constant([10,12,15,20])))\n",
         "\n",
-        "tf_f = autograph.to_graph(f)\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_f(tf.constant([10,12,15,20])).eval())"
+        "    print('Graph result: %d\\n\\n' % tf_sum_even(tf.constant([10,12,15,20])).eval())"
       ],
       "execution_count": 0,
       "outputs": []
@@ -332,7 +344,7 @@
       },
       "cell_type": "code",
       "source": [
-        "print(autograph.to_code(f))"
+        "print(autograph.to_code(sum_even))"
       ],
       "execution_count": 0,
       "outputs": []
@@ -360,15 +372,13 @@
         "@autograph.convert()\n",
         "def fizzbuzz(num):\n",
         "  if num % 3 == 0 and num % 5 == 0:\n",
-        "      print('FizzBuzz')\n",
+        "      return 'FizzBuzz'\n",
         "  elif num % 3 == 0:\n",
-        "      print('Fizz')\n",
+        "      return 'Fizz'\n",
         "  elif num % 5 == 0:\n",
-        "      print('Buzz')\n",
+        "      return 'Buzz'\n",
         "  else:\n",
-        "      print(num)\n",
-        "  return num\n",
-        "\n",
+        "      return tf.as_string(num)\n",
         "\n",
         "with tf.Graph().as_default():\n",
         "  # The result works like a regular op: takes tensors in, returns tensors.\n",
@@ -377,7 +387,7 @@
         "  result = fizzbuzz(num)\n",
         "  with tf.Session() as sess:\n",
         "    for n in range(10,16):\n",
-        "      sess.run(result, feed_dict={num:n})"
+        "      print(sess.run(result, feed_dict={num:n}))"
       ],
       "execution_count": 0,
       "outputs": []
@@ -415,14 +425,14 @@
       "cell_type": "code",
       "source": [
         "@autograph.convert()\n",
-        "def f(x):\n",
-        "  assert x != 0, 'Do not pass zero!'\n",
-        "  return x * x\n",
+        "def inverse(x):\n",
+        "  assert x != 0.0, 'Do not pass zero!'\n",
+        "  return 1.0/x\n",
         "\n",
         "with tf.Graph().as_default():  \n",
         "  with tf.Session():\n",
         "    try:\n",
-        "      print(f(tf.constant(0)).eval())\n",
+        "      print(inverse(tf.constant(0.0)).eval())\n",
         "    except tf.errors.InvalidArgumentError as e:\n",
         "      print('Got error message:\\n    %s' % e.message)"
       ],
@@ -443,30 +453,30 @@
     },
     {
       "metadata": {
-        "id": "ySTsuxnqCTQi",
+        "id": "ehBac9rUR6nh",
         "colab_type": "code",
         "colab": {}
       },
       "cell_type": "code",
       "source": [
         "@autograph.convert()\n",
-        "def f(n):\n",
-        "  if n >= 0:\n",
-        "    while n < 5:\n",
-        "      n += 1\n",
-        "      print(n)\n",
+        "def count(n):\n",
+        "  i=0\n",
+        "  while i < n:\n",
+        "    print(i)\n",
+        "    i += 1\n",
         "  return n\n",
         "    \n",
         "with tf.Graph().as_default():\n",
         "  with tf.Session():\n",
-        "    f(tf.constant(0)).eval()"
+        "    count(tf.constant(0)).eval()"
       ],
       "execution_count": 0,
       "outputs": []
     },
     {
       "metadata": {
-        "id": "NqF0GT-VCVFh",
+        "id": "mtpegD_YR6HK",
         "colab_type": "text"
       },
       "cell_type": "markdown",
@@ -485,10 +495,10 @@
       "cell_type": "code",
       "source": [
         "@autograph.convert()\n",
-        "def f(n):\n",
+        "def arange(n):\n",
         "  z = []\n",
         "  # We ask you to tell us the element dtype of the list\n",
-        "  autograph.utils.set_element_type(z, tf.int32)\n",
+        "  autograph.set_element_type(z, tf.int32)\n",
         "  \n",
         "  for i in range(n):\n",
         "    z.append(i)\n",
@@ -500,7 +510,7 @@
         "\n",
         "with tf.Graph().as_default():  \n",
         "  with tf.Session():\n",
-        "    print(f(tf.constant(3)).eval())"
+        "    print(arange(tf.constant(10)).eval())"
       ],
       "execution_count": 0,
       "outputs": []
@@ -512,7 +522,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "### Nested if statements"
+        "### Nested control flow"
       ]
     },
     {
@@ -746,13 +756,13 @@
         "  # to convert these lists into their graph equivalent,\n",
         "  # we need to specify the element type of the lists.\n",
         "  train_losses = []\n",
-        "  autograph.utils.set_element_type(train_losses, tf.float32)\n",
+        "  autograph.set_element_type(train_losses, tf.float32)\n",
         "  test_losses = []\n",
-        "  autograph.utils.set_element_type(test_losses, tf.float32)\n",
+        "  autograph.set_element_type(test_losses, tf.float32)\n",
         "  train_accuracies = []\n",
-        "  autograph.utils.set_element_type(train_accuracies, tf.float32)\n",
+        "  autograph.set_element_type(train_accuracies, tf.float32)\n",
         "  test_accuracies = []\n",
-        "  autograph.utils.set_element_type(test_accuracies, tf.float32)\n",
+        "  autograph.set_element_type(test_accuracies, tf.float32)\n",
         "  \n",
         "  # This entire training loop will be run in-graph.\n",
         "  i = tf.constant(0)\n",
