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Source code for ignite.metrics.loss

from __future__ import division

from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric


[docs]class Loss(Metric): """ Calculates the average loss according to the passed loss_fn. Args: loss_fn (callable): a callable taking a prediction tensor, a target tensor, optionally other arguments, and returns the average loss over all observations in the batch. output_transform (callable): a callable that is used to transform the :class:`~ignite.engine.Engine`'s `process_function`'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. The output is is expected to be a tuple (prediction, target) or (prediction, target, kwargs) where kwargs is a dictionary of extra keywords arguments. batch_size (callable): a callable taking a target tensor that returns the first dimension size (usually the batch size). """ def __init__(self, loss_fn, output_transform=lambda x: x, batch_size=lambda x: x.shape[0]): super(Loss, self).__init__(output_transform) self._loss_fn = loss_fn self._batch_size = batch_size def reset(self): self._sum = 0 self._num_examples = 0 def update(self, output): if len(output) == 2: y_pred, y = output kwargs = {} else: y_pred, y, kwargs = output average_loss = self._loss_fn(y_pred, y, **kwargs) if len(average_loss.shape) != 0: raise ValueError('loss_fn did not return the average loss.') N = self._batch_size(y) self._sum += average_loss.item() * N self._num_examples += N def compute(self): if self._num_examples == 0: raise NotComputableError( 'Loss must have at least one example before it can be computed.') return self._sum / self._num_examples

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