GitLab

* Refactor and use class-based model for Resnet

* Linting

* Cleanup

* Cleanup in lieu of MNIST changes

* Moving params to init function

* Making learning_rate function

* Testing remotely...

* Testing remotely...

* Testing remotely...

* Testing remotely...

* Testing remotely...

* Respond to CR pt 1

* Respond to CR pt 2

* Respond to CR pt 3

* Respond to CR pt 4

* Adding batch norm vars in

* Exclude batch norm vars as the default

* Fixing CIFAR-10 naming

* Adding multi-GPU code

* Git rewind

* Git add file

* Manual revert

* Fixing tests

* Allowing input thread specification

* Adding comments

* Allowing input thread specification

* Merging resnet files

* Merging resnet files

* Refactoring input methods to allow for Reed's improvements

* Adding comments

* Changing arg name

* Removing contrib shuffle_and_repeat

* Removing contrib shuffle_and_repeat

* Removing contrib shuffle_and_repeat

* Debugging

* Removing with dependency on update_op

* Updating comments.

* Returning dataset directly

* Adding newline

* Adding num_gpus flag

* Refining preprocessing, part 1

* Refinements to preprocessing resulting from multi-GPU tests

* Reviving one-hot labels

* Reviving one-hot labels

* Fixing label shapes

* Removing epoch leftovers

* Adding random flip back in

* Reverting unnecessary linting of test file

* Respond to CR

* Respond to CR

* Respond to CR

* Remove conversion to float

* Remove conversion to float- comment

* Making means full-scale

* Pulling data.take under multi_gpu flag

* Pulling data.take under multi_gpu flag- Cifar

* Pulling data.take under multi_gpu flag- Cifar

* Removing num_gpus

* Removing num_gpus

Add an example showing how to train the MNIST model with eager execution
enabled.

(This change requires changes to TensorFlow made after
the 1.6 release branch was cut, i.e., will require a build
from source or TensorFlow 1.7+)

* Refining preprocessing, part 1

* Refinements to preprocessing resulting from multi-GPU tests

* Reviving one-hot labels

* Reviving one-hot labels

* Fixing label shapes

* Adding random flip back in

* Reverting unnecessary linting of test file

* Respond to CR

* Respond to CR

* Respond to CR

The long path doesn't work for TensorFlow 1.6 because the .python package under cluster_resolver doesn't exist.

* Refactoring input function

* Updating name of variable

The `sparse` version is more efficient anyway.

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

Add multi-GPU option to MNIST

* Using nightly TF docker

* Explicitly pull image

* Update readme

* Respond to CR

The `sparse` version is more efficient anyway.

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

This reverts commit e8726907.

Add multi-GPU flag to MNIST and allow for setting of replicated optimizer and model_fn

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).