Source code for gluonnlp.initializer.initializer

# coding: utf-8

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# pylint: disable=
"""Highway layer initializer."""
from __future__ import absolute_import, print_function

__all__ = ['HighwayBias']

import mxnet
from mxnet.initializer import Initializer


[docs]@mxnet.initializer.register class HighwayBias(Initializer): r"""Initialize all biases of an Highway layer by setting the biases of nonlinear transformer and the transform gate differently. The dimension of the biases are identical and equals to the :math:`arr.shape[0]/2`, where :math:`arr` is the bias tensor. The definition of the biases follows the work:: @inproceedings{srivastava2015training, title={Training very deep networks}, author={Srivastava, Rupesh K and Greff, Klaus and Schmidhuber, J{\"u}rgen}, booktitle={Advances in neural information processing systems}, pages={2377--2385}, year={2015} } Parameters ---------- nonlinear_transform_bias: float, default 0.0 bias for the non linear transformer. We set the default according to the above original work. transform_gate_bias: float, default -2.0 bias for the transform gate. We set the default according to the above original work. """ def __init__(self, nonlinear_transform_bias=0.0, transform_gate_bias=-2.0, **kwargs): super(HighwayBias, self).__init__(**kwargs) self.nonlinear_transform_bias = nonlinear_transform_bias self.transform_gate_bias = transform_gate_bias def _init_weight(self, name, arr): # pylint: disable=unused-argument """Abstract method to Initialize weight.""" arr[:int(arr.shape[0] / 2)] = self.nonlinear_transform_bias arr[int(arr.shape[0] / 2):] = self.transform_gate_bias