# 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)
```