ivy.adam_update(ws, dcdws, lr, mw, vw, step, beta1=0.9, beta2=0.999, epsilon=1e-07, f=None)[source]

Update weights ws of some function, given the derivatives of some cost c with respect to ws, using ADAM update. [reference]

  • ws (container of variables) – Weights of the function to be updated.

  • dcdws (container of arrays) – Derivates of the cost c with respect to the weights ws, [dc/dw for w in ws].

  • lr (float) – Learning rate, the rate at which the weights should be updated relative to the gradient.

  • mw (container of arrays) – running average of the gradients

  • vw (container of arrays) – running average of second moments of the gradients

  • step (int) – training step

  • beta1 (float) – gradient forgetting factor

  • beta2 (float) – second moment of gradient forgetting factor

  • epsilon (float) – divisor during adam update, preventing division by zero

  • f (ml_framework, optional) – Machine learning framework. Inferred from inputs if None.


The new function weights ws_new, and also new mw and vw, following the gradient descent updates.

Supported Frameworks:

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