Automatic Differentiation and Gradients tf.GradientTape()

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
20/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
java, tensorflow
Domain
machine-learning

Research direction

Start by reviewing the tf.GradientTape API and the linked TensorFlow autodiff and training-loop documentation. The request proposes adding this capability to the tensorflow-core module, but does not identify Java entry points, files, tests, or a defined completion criterion; those details need to be established before implementation.

Written by the indexing model from the issue text.

Description

Please make sure that this is a feature request. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:feature_template

System information

  • TensorFlow version (you are using): 2.3.1
  • Are you willing to contribute it (Yes/No): Yes, when able and available

Describe the feature and the current behavior/state.
TensorFlow provides the tf.GradientTape API to differentiate automatically, TensorFlow needs to remember what operations happen in what order during the forward pass. Then, during the backward pass, TensorFlow traverses this list of operations in reverse order to compute gradients.

Details about this feature can be found in the official TensorFlow documentation for Gradient Tapes

Will this change the current api? How?
Yes, I think it will add a new feature to tensorflow-core module

Who will benefit with this feature?
Anyone that requires a very low-level control over training and evaluation of a deep learning model and everyone who is already familiar with TF/Keras.

Any Other info.
tf.GradientTape API is needed when writing a training loop from scratch as described here

Dominant language
Java
Stars
928
Forks
227
No merged PRs in 30d

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