GitLab

Use TensorFlow functions instead of Keras strings

* Sentiment analysis model initial implementation.

- This is an implementation of the Sentiment Analysis model.
- The implementation is with the reference to paddle version.

* Sentiment analysis model initial implementation.

- This is an implementation of the Sentiment Analysis model.
- The implementation is with the reference to paddle version.

* addressing comments

* addressing comments

* addressing the comments: using the util function for distribution strategy

* addressing comments

- lint style correction.
- avoid using global FLAG variable.

* modify the format to fit in the style guideline.

* convert from single quote to double quote

No-need to upgrade now that r1.9rc1 is up in Colab.

* begin branch

* finish download script

* rename download to dataset

* intermediate commit

* intermediate commit

* misc tweaks

* intermediate commit

* intermediate commit

* intermediate commit

* delint and update census test.

* add movie tests

* delint

* fix py2 issue

* address PR comments

* intermediate commit

* intermediate commit

* intermediate commit

* finish wide deep transition to vanilla movielens

* delint

* intermediate commit

* intermediate commit

* intermediate commit

* intermediate commit

* fix import

* add default ncf csv construction

* change default on download_if_missing

* shard and vectorize example serialization

* fix import

* update ncf data unittests

* delint

* delint

* more delinting

* fix wide-deep movielens serialization

* address PR comments

* add file_io tests

* investigate wide-deep test failure

* remove hard coded path and properly use flags.

* address file_io test PR comments

* missed a hash_bucked_size

Remove preprocess fn

Removed preprocess function

2. Flag that allows to prevent imagenet.py  from downloading label_to_names from github and/or dumping into training directory (which might be read-only)
3. Adds some comments about how decay steps are computed, since it computed differently when there are clones vs sync replicas.
4. Updates mobilenet.md to describe the training process using train_image_classifer
5. Add citation for TF-Slim model library.

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Rename eager notebook path, keep header

Remove string activations + typos.

Get started index

Save and restore

Fix too much big value of default max_steps of 2560 epoch to 256 epoch

Merge internal changes

Add the basic-classification notebook.

Add "basic-text-classification" notebook.

Add basic-regression

Add MIT license

Add Overfit and Underfit notebook.

LFADS: Support for posterior_push_mean instead of sampling