Step Closures

default_step_closure(x, y, model, criterion, optimizer, scores, max_grad_norm=1000, device='cpu', transform_grads=None, transform_y=None, **kwargs)

one step of local training including: * prepare mini batch of the data * forward pass * loss calculation * backward pass * transfor and modify the gradients * take optimization step * evaluate scores on the training mini-batch batch.

Parameters

• x (Tensor) -- inputs

• y (Tensor) -- labels

• model (Module) -- model

• criterion (Callable) -- loss criterion

• optimizer (Optimizer) -- optimizer chosen and instanciated from classes under torch.optim.

• scores -- Dict[str, Score]: dictionary of form str: Score to evaluate at the end of the closure.

• max_grad_norm (int, optional) -- to clip the norm of the gradients. Defaults to 1000.

• device (str, optional) -- device to load the data into ("cpu", "cuda", or device ordinal number). This must be the same device as the one model parameters are loaded into. Defaults to "cpu".

• transform_grads (Callable, optional) -- A function the takes the model and modified the gradients of the parameters. Defaults to None.

• transform_y (Callable, optional) -- a function that takes raw labels and modifies them. Defaults to None.

Returns

Tensor -- loss value obtained from the forward pass.