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', 'TruncNorm']

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
[docs]@mxnet.initializer.register class TruncNorm(Initializer): r"""Initialize the weight by drawing sample from truncated normal distribution with provided mean and standard deviation. Values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked.. Parameters ---------- mean : float, default 0 Mean of the underlying normal distribution stdev : float, default 0.01 Standard deviation of the underlying normal distribution **kwargs : dict Additional parameters for base Initializer. """ def __init__(self, mean=0, stdev=0.01, **kwargs): super(TruncNorm, self).__init__(**kwargs) try: from scipy.stats import truncnorm except ImportError: raise ImportError('SciPy is not installed. ' 'You must install SciPy >= 1.0.0 in order to use the ' 'TruncNorm. You can refer to the official ' 'installation guide in https://www.scipy.org/install.html .') self._frozen_rv = truncnorm(-2, 2, mean, stdev) def _init_weight(self, name, arr): # pylint: disable=unused-argument """Abstract method to Initialize weight.""" arr[:] = self._frozen_rv.rvs(arr.size).reshape(arr.shape)