Source code for gluonnlp.loss.activation_regularizer

# coding: utf-8

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# pylint: disable=
"""Language model RNN loss."""

__all__ = ['ActivationRegularizationLoss', 'TemporalActivationRegularizationLoss']

from mxnet.gluon.loss import Loss


[docs]class ActivationRegularizationLoss(Loss): r"""Computes Activation Regularization Loss. (alias: AR) The formulation is as below: .. math:: L = \alpha L_2(h_t) where :math:`L_2(\cdot) = {||\cdot||}_2, h_t` is the output of the RNN at timestep t. :math:`\alpha` is scaling coefficient. The implementation follows the work:: @article{merity2017revisiting, title={Revisiting Activation Regularization for Language RNNs}, author={Merity, Stephen and McCann, Bryan and Socher, Richard}, journal={arXiv preprint arXiv:1708.01009}, year={2017} } Parameters ---------- alpha : float, default 0 The scaling coefficient of the regularization. weight : float or None Global scalar weight for loss. batch_axis : int, default 0 The axis that represents mini-batch. """ def __init__(self, alpha=0, weight=None, batch_axis=None, **kwargs): super(ActivationRegularizationLoss, self).__init__(weight, batch_axis, **kwargs) self._alpha = alpha def __repr__(self): s = 'ActivationRegularizationLoss (alpha={alpha})' return s.format(alpha=self._alpha)
[docs] def hybrid_forward(self, F, *states): # pylint: disable=arguments-differ """ Parameters ---------- states : list the stack outputs from RNN, which consists of output from each time step (TNC). Returns -------- loss : NDArray loss tensor with shape (batch_size,). Dimensions other than batch_axis are averaged out. """ # pylint: disable=unused-argument if self._alpha != 0: if states: means = [self._alpha * state.__pow__(2).mean() for state in states[-1:]] return F.add_n(*means) else: return F.zeros(1) return F.zeros(1)
[docs]class TemporalActivationRegularizationLoss(Loss): r"""Computes Temporal Activation Regularization Loss. (alias: TAR) The formulation is as below: .. math:: L = \beta L_2(h_t-h_{t+1}) where :math:`L_2(\cdot) = {||\cdot||}_2, h_t` is the output of the RNN at timestep t, :math:`h_{t+1}` is the output of the RNN at timestep t+1, :math:`\beta` is scaling coefficient. The implementation follows the work:: @article{merity2017revisiting, title={Revisiting Activation Regularization for Language RNNs}, author={Merity, Stephen and McCann, Bryan and Socher, Richard}, journal={arXiv preprint arXiv:1708.01009}, year={2017} } Parameters ---------- beta : float, default 0 The scaling coefficient of the regularization. weight : float or None Global scalar weight for loss. batch_axis : int, default 0 The axis that represents mini-batch. """ def __init__(self, beta=0, weight=None, batch_axis=None, **kwargs): super(TemporalActivationRegularizationLoss, self).__init__(weight, batch_axis, **kwargs) self._beta = beta def __repr__(self): s = 'TemporalActivationRegularizationLoss (beta={beta})' return s.format(beta=self._beta)
[docs] def hybrid_forward(self, F, *states): # pylint: disable=arguments-differ """ Parameters ---------- states : list the stack outputs from RNN, which consists of output from each time step (TNC). Returns -------- loss : NDArray loss tensor with shape (batch_size,). Dimensions other than batch_axis are averaged out. """ # pylint: disable=unused-argument if self._beta != 0: if states: means = [self._beta * (state[1:] - state[:-1]).__pow__(2).mean() for state in states[-1:]] return F.add_n(*means) else: return F.zeros(1) return F.zeros(1)