| import torch |
| from .Module import Module |
| |
| # This module acts as an L1 latent state regularizer, adding the |
| # [gradOutput] to the gradient of the L1 loss. The [input] is copied to |
| # the [output]. |
| |
| |
| class L1Penalty(Module): |
| |
| def __init__(self, l1weight, sizeAverage=False, provideOutput=True): |
| super(L1Penalty, self).__init__() |
| self.l1weight = l1weight |
| self.sizeAverage = sizeAverage |
| self.provideOutput = provideOutput |
| |
| def updateOutput(self, input): |
| m = self.l1weight |
| if self.sizeAverage: |
| m = m / input.nelement() |
| |
| loss = m * input.norm(1) |
| self.loss = loss |
| self.output = input |
| return self.output |
| |
| def updateGradInput(self, input, gradOutput): |
| m = self.l1weight |
| if self.sizeAverage: |
| m = m / input.nelement() |
| |
| self.gradInput.resize_as_(input).copy_(input).sign_().mul_(m) |
| |
| if self.provideOutput: |
| self.gradInput.add_(gradOutput) |
| |
| return self.gradInput |
