GitLab

* Revert 823da318. This restores distribution strategies for resnet.

This commit is not a direct revert due to significant merge conflict
resolution.

* fix flags test

* npc is no longer used in resnet

* fix treatment of main() and absl flags

* add mnist_eager, and delint

* add mnist_eager docstring

* squash of modular absl usage commits

* delint

* address PR comments

* change hooks to comma separated list, as absl behavior for space separated lists is not as expected

Add buttons and enable TOC

official/mnist: Updates with the release of TensorFlow 1.8.

* Adding option for one_box_for_all_classes to the box_predictor

PiperOrigin-RevId: 192813444

* Extend to accept different ratios of conv channels.

PiperOrigin-RevId: 192837477

* Remove inaccurate caveat from proto file.

PiperOrigin-RevId: 192850747

* Add option to set dropout for classification net in weight shared box predictor.

PiperOrigin-RevId: 192922089

* fix flakiness in testSSDRandomCropWithMultiClassScores due to randomness.

PiperOrigin-RevId: 193067658

* Post-process now works again in train mode.

PiperOrigin-RevId: 193087707

* Adding support for reading in logits as groundtruth labels and applying an optional temperature (scaling) before softmax in support of distillation.

PiperOrigin-RevId: 193119411

* Add a util function to visualize value histogram as a tf.summary.image.

PiperOrigin-RevId: 193137342

* Do not add batch norm parameters to final conv2d ops that predict boxes encodings and class scores in weight shared conv box predictor.

This allows us to set proper bias and force initial predictions to be background when using focal loss.

PiperOrigin-RevId: 193204364

* Make sure the final layers are also resized proportional to conv_depth_ratio.

PiperOrigin-RevId: 193228972

* Remove deprecated batch_norm_trainable field from ssd mobilenet v2 config

PiperOrigin-RevId: 193244778

* Updating coco evaluation metrics to allow for a batch of image info, rather than a single image.

PiperOrigin-RevId: 193382651

* Update protobuf requirements to 3+ in installation docs.

PiperOrigin-RevId: 193409179

* Add support for training keypoints.

PiperOrigin-RevId: 193576336

* Fix data augmentation functions.

PiperOrigin-RevId: 193737238

* Read the default batch size from config file.

PiperOrigin-RevId: 193959861

* Fixing a bug in the coco evaluator.

PiperOrigin-RevId: 193974479

* num_gt_boxes_per_image and num_det_boxes_per_image value incorrect.
Should be not the expand dim.

PiperOrigin-RevId: 194122420

* Add option to evaluate any checkpoint (without requiring write access to that directory and overwriting any existing logs there).

PiperOrigin-RevId: 194292198

* PiperOrigin-RevId: 190346687

* - Expose slim arg_scope function to compute keys to enable tessting.
- Add is_training=None option to mobinenet arg_scopes. This allows the users to set is_training from an outer scope.

PiperOrigin-RevId: 190997959

* Add an option to not set slim arg_scope for batch_norm is_training parameter. This enables users to set the is_training parameter from an outer scope.

PiperOrigin-RevId: 191611934

* PiperOrigin-RevId: 191955231

* PiperOrigin-RevId: 193254125

* PiperOrigin-RevId: 193371562

* PiperOrigin-RevId: 194085628

* catch cpuinfo ImportError

* add psutil import catch

* fix typo

[samples/core/get_starter/eager]: Update with API simplifications in 1.8

Add marco model: automating the evaluation of crystallization experiments

* Add new import paths to mnist_tpu.py

This adds new import paths to mnist_tpu.py so individuals can check out the git repository and run `python mnist_tpu.py` without finagling with python import search paths.

* Fix lint errors

* Add disable= to pylint instructions

* Remove parent path

* Update comments

Refer to commit "use reduce_mean instead of Average Pooling"(d5663b3c), "second_pool_size" and "second_pool_stride" no longer needed in resnet_model

Increase minimum TF version for DEFINE_enum and rename variable mappings for change to RNN variable names. (#4123)

fix import errors and type error in numpy for the PATE implementation

1. trainning hooks and train/eval function does not need to be
declared several times.
2. change to use tf.logging.info instead of print
3. Show the current index and total count of training cycle, which
give user some hint about where they are in the whole process.

* + plot_partition.py (budget allocation with smooth sensitivity).
+ utility_queries_answered.py (Figure 1, left).
+ several changes to the scripts required for the ICLR poster.

* Adding Ananth Raghunathan (pseudorandom@) to the list of owners of differential_privacy.

* Fixing the owners list of differential_privacy.

Fix 2 typos in eager.ipynb

Fixed #4083 and two points of optimizations

1. Solved by adding cast with tf.cast(predictions, tf.float64) at the point of
problem case.
2. Completed make_dataset() of automobile_data.py for the transforming of
DataFrame to Dataset, the standard one we know of official site.
3. Added from_dataset() in automobile_data.py for re-use by others sites in
cookbook/regression.

* Add minigo unit test

* Add minigo unit test

* Fix gpylints and update readme