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use nbviewer for some of the notebook header links in the readme #2

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amygdala merged 2 commits into from
Jan 16, 2018
Merged

use nbviewer for some of the notebook header links in the readme #2

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a100832
added a notebook header
amygdala Jan 16, 2018
e94a879
use nbviewer for some of the notebook links in the readme
amygdala Jan 16, 2018
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4 changes: 2 additions & 2 deletions ml/census_train_and_eval/README.md
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Expand Up @@ -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:

```
Expand Down Expand Up @@ -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.


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28 changes: 21 additions & 7 deletions ml/census_train_and_eval/using_tf.estimator.train_and_evaluate.ipynb
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Expand Up @@ -106,7 +106,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import division\n",
Expand Down Expand Up @@ -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`"
]
},
{
Expand Down Expand Up @@ -878,7 +882,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!cat config_custom_gpus.yaml"
Expand All @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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",
Expand All @@ -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",
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