From a10083217d453968ab2428485b1afb0d9b5f2d26 Mon Sep 17 00:00:00 2001
From: Amy Unruh <amyu@google.com>
Date: Tue, 16 Jan 2018 07:42:34 -0800
Subject: [PATCH 1/2] added a notebook header

---
 ...sing_tf.estimator.train_and_evaluate.ipynb | 28 ++++++++++++++-----
 1 file changed, 21 insertions(+), 7 deletions(-)

diff --git a/ml/census_train_and_eval/using_tf.estimator.train_and_evaluate.ipynb b/ml/census_train_and_eval/using_tf.estimator.train_and_evaluate.ipynb
index c0b3634..5d71b8e 100644
--- a/ml/census_train_and_eval/using_tf.estimator.train_and_evaluate.ipynb
+++ b/ml/census_train_and_eval/using_tf.estimator.train_and_evaluate.ipynb
@@ -106,7 +106,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from __future__ import division\n",
@@ -675,7 +677,9 @@
     "If you take a look in the `trainer` subdirectory of this directory, you'll see that it contains essentially the same code that's in this notebook, just packaged for deployment.  `trainer.task` is the entry point, and when that file is run, it calls `tf.estimator.train_and_evaluate`.  \n",
     "(You can read more about how to package your code [here](https://cloud.google.com/ml-engine/docs/packaging-trainer)).  \n",
     "\n",
-    "We'll test training via `gcloud` locally first, to make sure that we have everything packaged up correctly."
+    "We'll test training via `gcloud` locally first, to make sure that we have everything packaged up correctly.\n",
+    "\n",
+    "### Test training locally via `gcloud`"
    ]
   },
   {
@@ -878,7 +882,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "!cat config_custom_gpus.yaml"
@@ -898,7 +904,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "job_name = \"census_job_%s\" % (int(time.time()))\n",
@@ -914,7 +922,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "!gcloud ml-engine jobs submit training $JOB_NAME --scale-tier $SCALE_TIER \\\n",
@@ -958,7 +968,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# We'll use the `hptuning_config.yaml` file for this run.\n",
@@ -968,7 +980,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "!gcloud ml-engine jobs submit training $JOB_NAME --scale-tier $SCALE_TIER \\\n",

From e94a879cfcfc3dd1112e8cbbc68293f1fec4fcc3 Mon Sep 17 00:00:00 2001
From: Amy Unruh <amyu@google.com>
Date: Tue, 16 Jan 2018 11:52:16 -0800
Subject: [PATCH 2/2] use nbviewer for some of the notebook links in the readme

---
 ml/census_train_and_eval/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ml/census_train_and_eval/README.md b/ml/census_train_and_eval/README.md
index 4e37532..8954952 100644
--- a/ml/census_train_and_eval/README.md
+++ b/ml/census_train_and_eval/README.md
@@ -35,7 +35,7 @@ You can read more about this model and its use [here](https://research.googleblo
 
 We're using Estimators because they give us built-in support for distributed training and evaluation (along with other nice features). You should nearly always use Estimators to create your TensorFlow models. You can build a Custom Estimator if none of the pre-made Estimators suit your purpose.
 
-See the accompanying [notebook](using_tf.estimator.train_and_evaluate.ipynb) for the details of defining our Estimator, including specifying the expected format of the input data.
+See the accompanying [notebook](https://nbviewer.jupyter.org/github/amygdala/code-snippets/blob/master/ml/census_train_and_eval/using_tf.estimator.train_and_evaluate.ipynb#First-step:-create-an-Estimator) for the details of defining our Estimator, including specifying the expected format of the input data.
 The data is in csv format, and looks like this:
 
 ```
@@ -83,7 +83,7 @@ The `Dataset` API is much more performant than using `feed_dict` or the queue-ba
 
 In this simple example, our datasets are too small for the use of the Datasets API to make a large difference, but with larger datasets it becomes much more important.
 
-The `input_fn` definition is the following. It uses a couple of helper functions that are defined in the [notebook](using_tf.estimator.train_and_evaluate.ipynb).    
+The `input_fn` definition is the following. It uses a couple of helper functions that are defined in the [notebook](https://nbviewer.jupyter.org/github/amygdala/code-snippets/blob/master/ml/census_train_and_eval/using_tf.estimator.train_and_evaluate.ipynb#Define-input-functions-(using-Datasets)).    
 `parse_label_column` is used to convert the label strings (in our case, ' <=50K' and ' >50K') into [one-hot](https://en.wikipedia.org/wiki/One-hot) encodings.