optimizer_update(ws, effective_grads, lr, inplace=True, stop_gradients=True)¶
Update weights ws of some function, given the true or effective derivatives of some cost c with respect to ws, [dc/dw for w in ws].
ws (Ivy container) – Weights of the function to be updated.
effective_grads (Ivy container) – Effective gradients of the cost c with respect to the weights ws, [dc/dw for w in ws].
lr (float or container of layer-wise rates.) – Learning rate(s), the rate(s) at which the weights should be updated relative to the gradient.
inplace (bool, optional) – Whether to perform the operation inplace, for backends which support inplace variable updates, and handle gradients behind the scenes such as PyTorch. If the update step should form part of a computation graph (i.e. higher order optimization), then this should be set to False. Default is True.
stop_gradients (bool, optional) – Whether to stop the gradients of the variables after each gradient step. Default is True.
The new function weights ws_new, following the optimizer updates.