GitLab

Stub moved notebooks.

* Add top_5 to to eval.

* labels shape to [?] from [?,1] matches unittest.

Typo fix

Update the runtime version in bash script

The bash script to submit training job for pets detection has runtime-version of 1.8. This will trigger `TypeError: non_max_suppression() got an unexpected keyword argument 'score_threshold'` on the Google Cloud since 1.8 and older does not support this keyword argument. Therefore, update this runtime version to 1.9, which is the most recent runtime version that is published on June 27, 2018. 

See 
https://github.com/tensorflow/models/issues/5056
https://cloud.google.com/ml-engine/docs/tensorflow/runtime-version-list

Updated model_lib to use min_score_threshold and max_num_boxes_to_visualize from the eval config.

Text classification tutorial clarification

Rename "num_examples" to "num_reviews" to be consistent with the "one-hot-encode" size description: "num_words * num_reviews".

* Fix convergence issues for MLPerf.

Thank you to @robieta for helping me find these issues, and for providng an algorithm for the `get_hit_rate_and_ndcg_mlperf` function.

This change causes every forked process to set a new seed, so that forked processes do not generate the same set of random numbers. This improves evaluation hit rates.

Additionally, it adds a flag, --ml_perf, that makes further changes so that the evaluation hit rate can match the MLPerf reference implementation.

I ran 4 times with --ml_perf and 4 times without. Without --ml_perf, the highest hit rates achieved by each run were 0.6278, 0.6287, 0.6289, and 0.6241. With --ml_perf, the highest hit rates were 0.6353, 0.6356, 0.6367, and 0.6353.

* fix lint error

* Fix failing test

* Address @robieta's feedback

* Address more feedback

These have all moved to https://github.com/tensorflow/docs/tree/master/site/en

Basic_Regression notebook: Python2 compatibility

Docs: Grammar fix

* perform a codecs check and remove unicode \ufeff if utf-8 is not present

* delint

Docs: fix iteration count

Update the epoc/iteration count noted to match the code.

Update plt.grid args to avoid deprecation message

This is done by using a higher Pickle protocol version, which the Python docs describe as being "slightly more efficient". This reduces the file write time at the beginning from 2 1/2 minutes to 5 seconds.

Modify flag name for the checkpoint path

* Deterministic dataset order fix

In order for the order of the files to be deterministic, in `tf.data.Dataset.list_files(..., shuffle)`, shuffle needs to be True, otherwise different iterator inits will yield different file orders

* removed unnecessary shuffle of filenames

* Removed the `_FILE_SHUFFLE_BUFFER` definition

Adding illustrative charts to basic classification tutorial

Nits in basic_regression.ipynb

* Remove trailing spaces
* Fix "dependant" spelling error
* Remove periods at the end of comments*
* Add spaces after commas*

\* For internal consistency and consistency with PEP8 examples

Add a pointer to a Colab for the embeddings

Added a pointer to a Colab that illustrates how to use the public AudioSet models to generate embeddings for user-specified sounds.

* Fix Transformer TPU crash in Python 2.X.

- Tensorflow raises an error when tf_inspect.getfullargspec is called on
a functools.partial in Python 2.X. This issue would be hit during the
eval stage of the Transformer TPU model. This change replaces the call
to functools.partial with a lambda to work around the issue.

* Remove unused import from transformer_main.

* Fix lint error.

* warm start a resent with all but the dense layer and only update the final layer weights when fine tuning

* Update README for Transfer Learning

* make lint happy and variable naming error related to scaled gradients

* edit the test cases for cifar10 and imagenet to reflect the default case of no fine tuning