Step Closures#

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

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.

  • 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.


Tensor -- loss value obtained from the forward pass.