Automatic Differentiation and Gradients tf.GradientTape()

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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

Commencez par examiner l’API tf.GradientTape et la documentation liée sur l’autodifférentiation et les boucles d’entraînement de TensorFlow. La demande propose d’ajouter cette fonctionnalité au module tensorflow-core, mais n’identifie ni points d’entrée Java, ni fichiers, ni tests, ni critère d’achèvement défini ; ces détails doivent être établis avant l’implémentation.