Resolve review comments.

MarkDaoust · MarkDaoust · commit 0f5c10c57898 · 2018-07-13T10:41:38.000-07:00
diff --git a/samples/core/tutorials/estimators/wide.ipynb b/samples/core/tutorials/estimators/wide.ipynb
@@ -58,7 +58,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "# TensorFlow Linear Model Tutorial\n",
+        "# Build a classifier using Estimators\n",
         "\n",
         "<table class=\"tfo-notebook-buttons\" align=\"left\"><td>\n",
         "<a target=\"_blank\"  href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/tutorials/estimators/wide.ipynb\">\n",
@@ -75,23 +75,17 @@
       "cell_type": "markdown",
       "source": [
         "In this tutorial, we will use the `tf.estimator` API in TensorFlow to solve a\n",
-        "standard benchmark binary classification problem: Given census data about a \n",
-        "person such as age, education, marital status, and occupation (the features),\n",
-        "we will try to predict whether or not the person earns more than 50,000 dollars\n",
-        "a year (the target label). We will train a **logistic regression** model, and given \n",
-        "an individual's information our model will output a number between 0 and 1, which\n",
-        "can be interpreted as the probability that the individual has an annual income of over\n",
-        "50,000 dollars.\n",
+        "benchmark binary classification problem.\n",
         "\n",
-        "Key Point: As a modeler and developer, think about how this data is used and the potential benefits and harm a model's predictions can cause. A model like this could reinforce societal biases and disparities. Is each  feature relevant to the problem you want to solve or will it introduce bias? For more information, read about [ML fairness](https://developers.google.com/machine-learning/fairness-overview/).\n",
+        "Estimators are TensorFlow's most scalable and production oriented type of model. For more information see the [Estimator guide](../../guide/estimators).\n",
         "\n",
-        "## Setup\n",
+        "The problem is: Given census data about a person such as age, education, marital status, and occupation (the features), we will try to predict whether or not the person earns more than 50,000 dollars a year (the target label). We will train a **logistic regression** model, and given an individual's information our model will output a number between 0 and 1, which can be interpreted as the probability that the individual has an annual income of over 50,000 dollars.\n",
         "\n",
-        "To try the code for this tutorial:\n",
+        "Key Point: As a modeler and developer, think about how this data is used and the potential benefits and harm a model's predictions can cause. A model like this could reinforce societal biases and disparities. Is each  feature relevant to the problem you want to solve or will it introduce bias? For more information, read about [ML fairness](https://developers.google.com/machine-learning/fairness-overview/).\n",
         "\n",
-        "[Install TensorFlow](tensorlfow.org/install) if you haven't already.\n",
+        "## Setup\n",
         "\n",
-        "Next import the relavant packages:"
+        "To try this tutorial, first import the relavant packages:"
       ]
     },
     {
@@ -119,6 +113,17 @@
         "colab_type": "text"
       },
       "cell_type": "markdown",
+      "source": [
+        "## Official implementation\n",
+        "\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "tJqF5E6rtyCI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
       "source": [
         "Download the [tutorial code from github](https://github.com/tensorflow/models/tree/master/official/wide_deep/),\n",
         " add the root directory to your python path, and jump to the `wide_deep` directory:"
@@ -159,7 +164,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "Execute the data download script:"
+        "Download the dataset:"
       ]
     },
     {
@@ -185,6 +190,10 @@
       },
       "cell_type": "markdown",
       "source": [
+        "### Command line usage\n",
+        "\n",
+        "The repo includes a complete program for experimenting with this type of model.\n",
+        "\n",
         "To execute the tutorial code from the command line first add the path to tensorflow/models to your `PYTHONPATH`."
       ]
     },
@@ -248,15 +257,23 @@
       "execution_count": 0,
       "outputs": []
     },
+    {
+      "metadata": {
+        "id": "Uo2qoafut4MK",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Read on to find out how this code builds its models."
+      ]
+    },
     {
       "metadata": {
         "id": "AmZ4CpaOcYvV",
         "colab_type": "text"
       },
       "cell_type": "markdown",
       "source": [
-        "Read on to find out how this code builds its linear model.\n",
-        "\n",
         "## Reading The Census Data\n",
         "\n",
         "The dataset we're using is the\n",
@@ -1302,28 +1319,16 @@
       },
       "cell_type": "markdown",
       "source": [
-        "## What Next\n",
+        "## Next Steps\n",
         "\n",
-        "For more about estimators:\n",
+        "For more about estimators see:\n",
         "\n",
-        "- The [TensorFlow Hub text classification tutorial](https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub) uses `hub.text_embedding_column` to easily ingest free form text. \n",
+        "- The [Estimator Guide](tensorlfow.org/guide/estimators).\n",
+        "- The [TensorFlow Hub text classification tutorial](https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub), which uses `hub.text_embedding_column` to easily ingest free form text. \n",
         "- The [Gradient-boosted-trees estimator tutorial](https://github.com/tensorflow/models/tree/master/official/boosted_trees)\n",
         "- This [blog post]( https://medium.com/tensorflow/classifying-text-with-tensorflow-estimators) on processing text with `Estimators`\n",
         "- How to [build a custom CNN estimator](https://www.tensorflow.org/tutorials/estimators/cnn)"
       ]
-    },
-    {
-      "metadata": {
-        "id": "amMnupRPVtsa",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "cell_type": "code",
-      "source": [
-        ""
-      ],
-      "execution_count": 0,
-      "outputs": []
     }
   ]
 }
