GitLab

Fix bug in gym_wrapper.

Updates to pcl_rl according to most recent version of Trust-PCL paper

Fix #3148

Update README files to point to arXiv submission.

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

Adding myself to owners

Update compile instructions in embedding/README.md

Fix Issue : Change exporter.py in object_detection

[mnist]: Training and evaluation with TPUs.

Added an 'env' parameter to env_step function since one an unreferenced one is used in the function.

Incorporated the suggested changes.

[blog_estimators_dataset.py] bug fix for failing demo

Fix spacing in BUILD file comments.

Open source release of Brain Coder.

update supported tf versions for object detection on gcloud.

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).

Fix demo_inference to properly normalize input.

Use temp variable for predictions.

Before the fix the demo_inference.py used batch_norm and it did the
normalization of input image implicitly. If at inference time the
batch_norm was disabled the inference produced incorrect results.
This fix does the proper input image normalization and disables the batch_norm
at inference time.

Revert "Flags"

Change in Object Dectection Model. Change parameter name given to RewriterConfig Proto. optimize_tensor_layout was changed to layout_optimizer in current tf version.

Update Dockerfile with new bazel version.

Update README.md

Update README.md compile instructions to match adding_an_op.
from [adding_an_op](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/docs_src/extend/adding_an_op.md).

Tested: compiled using the modified instructions and ran `python word2vec_optimized_test.py`.

Fixes tensorflow/tensorflow#14620

Fix AudioSet url

[mnist]: Use FixedLengthRecordDataset

- 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)