tabensemb.model.AbstractNN.manual_backward

method

AbstractNN.manual_backward(loss: Tensor, *args: Any, **kwargs: Any) → None

Call this directly from your training_step() when doing optimizations manually. By using this, Lightning can ensure that all the proper scaling gets applied when using mixed precision.

See manual optimization for more examples.

Example:

def training_step(...):
    opt = self.optimizers()
    loss = ...
    opt.zero_grad()
    # automatically applies scaling, etc...
    self.manual_backward(loss)
    opt.step()

Parameters:

• loss – The tensor on which to compute gradients. Must have a graph attached.

• *args – Additional positional arguments to be forwarded to backward()

• **kwargs – Additional keyword arguments to be forwarded to backward()