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Copy edits following Mark's updates.

Set private_outputs (so output is not recorded on save).

Object detection export dir

* Update the wide_deep code for latest benchmark config.

* Also update the transformer benchmark code.

Release ssdlite mobilenet v2 coco trained model, add quantized training and minor fixes.

* Move the benchmark_uploader to new location.

* Update benchmark logger to streaming upload.

* Fix lint and unit test error.

* delint.

* Update the benchmark uploader test.

Skip the import of benchmark_uploader when bigquery is not installed.

* Merge the 2 classes of benchmark uploader into 1.

* Address review comments.

* delint.

* Execute bigquery upload in a separate thread.

* Change to use python six.moves for importing.

* Address review comments and delint.

* Address review comment.

Adding comment for potential performance impact for model on CPU.

* Fix random failure on py3.

* Fix the order of flag saver to avoid the randomness.

The test is broken when the benchmark_logger_type is set first, and
validated when the benchmark_log_dir is not set yet.

Export @195097388.

Update mobilenet_v1.md

196161788  by Zhichao Lu:

    Add eval_on_train_steps parameter.

    Since the number of samples in train dataset is usually different to the number of samples in the eval dataset.

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196151742  by Zhichao Lu:

    Add an optional random sampling process for SSD meta arch and update mean stddev coder to use default std dev when corresponding tensor is not added to boxlist field.

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196148940  by Zhichao Lu:

    Release ssdlite mobilenet v2 coco trained model.

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196058528  by Zhichao Lu:

    Apply FPN feature map generation before we add additional layers on top of resnet feature extractor.

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195818367  by Zhichao Lu:

    Add support for exporting detection keypoints.

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195745420  by Zhichao Lu:

    Introduce include_metrics_per_category option to Object Detection eval_config.

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195734733  by Zhichao Lu:

    Rename SSDLite config to be more explicit.

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195717383  by Zhichao Lu:

    Add quantized training to object_detection.

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195683542  by...

The final BN and ReLU layer is only need for v2 model since it was
doing preactivation in each block.

Addressing reviewers' comments. Added correct handling of order=np.inf.

* Add benchmark logging for wide_deep.

* Fix lint error.

Update mnist_eager.py to use GPU if available and not specified otherwise. At the moment it uses the CPU even if a GPU is available, since "if flags_obj.no_gpu or tf.test.is_gpu_available():" in "def run_mnist_eager(flags_obj):" evaluates to True if a GPU is available.

* forbid resnet v1 fp16

* address PR comments

Updates to differential_privacy/pate following the Banff workshop.

Collapse license cell

Fix typo in iris_data.py