GitLab

* Add recommendation model

* Fix pylints check error

* Rename file

* Address comments, update input pipeline, and add distribution strategy

* Fix import error

* Address more comments

* Fix lints

Update model_zoo.md

Fix broken table.

Nobody (I hope) is running TensorFlow 0.12 anymore.

PiperOrigin-RevId: 197225788

Update ADE20K dataset info.

Added change log

* Add boosted_trees to the official models

* Comments addressed from review, and a test added; using absl.flags instead of argparser.

* Used help_wrap. Also added instructions for inference.

[samples/java]: Explain where tensor names came from.

Fix #4013

* deprecate research/resnet

* also add a tf warning in the code

Fixes https://github.com/tensorflow/tensorflow/issues/19348

* Fix resnet missing layers.

The official v1 model contains BN and Relu between input layer and
pooling.

* Remove the BN and Relu for V2.

After some discussion with team and refer to existing
implementation, those two layer seems to be only useful in V1.
In V2, the first unit of the block will have a projection, that
apply the BN and Relu for the shortcut. Adding a comment to make
this clear.

* Expand the comment section.

* Remove the pre-trained checkpoint since its broken right now.

Will restore it once we have new checkpoint generated.

* Make codes more pythonic.

* Restore the indents

Restore the indents.

Fixed Bug regarding tfrecord shuffling in object_detection

Add lamberta to CODEOWNERS/samples

Add @lamberta to CODEOWNERS/samples

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.