GitLab

Use float32 metrics in mnist_eager

Deep Contextual Bandits code for tensorflow/models

Fix notebook text

Updating comments in export_tflite_ssd_graph scripts

float32 should be fine for mnist loss and accuracy metrics and float64
is not available on TPUs.

* Sentiment analysis implmenttion in pure Keras.

- This is an update for the sentiment analysis model's pure Keras version.
-- Converting it from the version using Tensorflow's estimator, as it has a issue that affects the accuracy of the model negatively.
- The implementation is with the reference to paddle version.
-- Adjustment of the hyperparameters was done to achieve the accuracy of ~90%

* addressing comments

* addressig comments

also adding

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

for each module as it seemingly is a standard in the repo.

* addressing the final comment.

Fixed minor bug in STEVE which sometimes prevented resuming experiments.

* Add more arguments

* Add eager mode

* Add notes for eager mode

* Address the comments

* Fix argument typos

* Add warning for eager and multi-gpu

* Fix typo

* Fix notes

* Fix pylint

Correcting comments

test data -> training data

Add autograph keras layers example

Update model zoo quantized model links.

[official/mnist]: Avoid some now unnecessary 'tfe' symbols.