tabensemb.model.AbstractNN.backward

method

AbstractNN.backward(loss: Tensor, optimizer: Steppable | None, optimizer_idx: int | None, *args: Any, **kwargs: Any) → None

Called to perform backward on the loss returned in training_step(). Override this hook with your own implementation if you need to.

Parameters:

• loss – The loss tensor returned by training_step(). If gradient accumulation is used, the loss here holds the normalized value (scaled by 1 / accumulation steps).

• optimizer – Current optimizer being used. None if using manual optimization.

• optimizer_idx – Index of the current optimizer being used. None if using manual optimization.

Example:

def backward(self, loss, optimizer, optimizer_idx):
    loss.backward()