GitLab

* Glint everything

* Adding rcfile and pylinting

* Extra newline

* Few last lints

The `sparse` version is more efficient anyway.

I'm returning the labels shape [1] instead of []
because tf.accuracy fails otherwise.

The `sparse` version is more efficient anyway.

I'm returning the labels shape [1] instead of []
because tf.accuracy fails otherwise.

Opening gzipped datasets in binary, read-only mode fixes the issue

This is a step towards merging the example in
https://github.com/tensorflow/tpu-demos/tree/master/cloud_tpu/models/mnist
with this repository, so we have a single model definition for
training across CPU/GPU/eager execution/TPU.

The change to dataset.py is so that the raw data can be read
from cloud storage systems (like GCS and S3).

- Prior to this change, the use of tf.data.Dataset essentially embedded
  the entire training/evaluation dataset into the graph as a constant,
  leading to unnecessarily humungous graphs (Fixes #3017)
- Also, use batching on the evaluation dataset to allow
  evaluation on GPUs that cannot fit the entire evaluation dataset in
  memory (Fixes #3046)