GitLab

[mnist]: Make flags function scoped (not module scoped).

This will make it easier to share the model definition with
eager execution and TPU demos without any side effects of running
unnecessary code on module import.

[mnist]: Updates

Update blog_custom_estimators.py

Added correct link to blogpost
Changed Sentosa to Setosa in a couple of places

https://github.com/tensorflow/models/pull/3024

Update README.md in adv_imagenet_models.

Add newly released adversarially trained (no ensemble) checkpoint for
Inception Resnet v2.

Add code for Custom Estimators blog post.

- Use the object-oriented tf.layers API instead of the functional one.
  The object-oriented API is particularly useful when using the model
  with eager execution.
- Update unittest to train, evaluate, and predict using the model.
- Add a micro-benchmark for measuring step-time.
  The parameters (batch_size, num_steps etc.) have NOT been tuned,
  the purpose with this code is mostly to illustrate how model
  benchmarks may be written.

These changes are made as a step towards consolidating model definitions
for different TensorFlow features (like eager execution and support for
TPUs in
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/mnist
and
https://github.com/tensorflow/tpu-demos/tree/master/cloud_tpu/models/mnist

With examples, and updates to the README

Fix the CODEOWNERS file syntax

Update data URL for CIFAR dataset (http->https).

Change docstring to refer to tf.estimator instead of tf.learn in official/wide_deep

Fix a bug in create_pet_tf_record.py related to mask creation.

Add a pre-trained ResNet-50 checkpoint

fix eval_training_data bug

Update tutorials/rnn/README.md with the correct Quick Draw link

[mnist]: Tweaks

Update exporting_models.md

Update path to tf_record script

* Split iris data

* move shared code to iris_data.py

* add minimal csv example

* remove unused pandas import

* Use sparse softmax loss to avoid warning

- Remove `convert_to_records.py` and instead create `tf.data.Dataset`
  objects directly from the numpy arrays.
- Format the Google Python Style (https://github.com/google/yapf/)

Adding TCN.