Source code for gluonnlp.model.highway

# coding: utf-8

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"""Highway layer."""

from __future__ import absolute_import
from __future__ import print_function

__all__ = ['Highway']

from mxnet import gluon
from mxnet.gluon import nn
from gluonnlp.initializer import HighwayBias


[docs]class Highway(gluon.HybridBlock): r"""Highway network. We implemented the highway network proposed in the following work:: @article{srivastava2015highway, title={Highway networks}, author={Srivastava, Rupesh Kumar and Greff, Klaus and Schmidhuber, J{\"u}rgen}, journal={arXiv preprint arXiv:1505.00387}, year={2015} } The full version of 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} } A Highway layer is defined as below: .. math:: y = (1 - t) * x + t * f(A(x)) which is a gated combination of a linear transform and a non-linear transform of its input, where :math:`x` is the input tensor, :math:`A` is a linear transformer, :math:`f` is an element-wise non-linear transformer, and :math:`t` is an element-wise transform gate, and :math:`1-t` refers to carry gate. Parameters ---------- input_size : int The dimension of the input tensor. We assume the input has shape ``(batch_size, input_size)``. num_layers : int The number of highway layers to apply to the input. activation : str, default 'relu' The non-linear activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`). highway_bias : HighwayBias, default HighwayBias(nonlinear_transform_bias=0.0, transform_gate_bias=-2.0) The biases applied to the highway layer. We set the default according to the above original work. """ def __init__(self, input_size, num_layers, activation='relu', highway_bias=HighwayBias(nonlinear_transform_bias=0.0, transform_gate_bias=-2.0), **kwargs): super(Highway, self).__init__(**kwargs) self._input_size = input_size self._num_layers = num_layers with self.name_scope(): self.hnet = nn.HybridSequential() with self.hnet.name_scope(): for _ in range(self._num_layers): self.hnet.add(nn.Dense(units=self._input_size * 2, in_units=self._input_size, bias_initializer=highway_bias, use_bias=True, flatten=False)) self._activation = nn.Activation(activation)
[docs] def hybrid_forward(self, F, inputs, **kwargs): # pylint: disable=arguments-differ # pylint: disable=unused-argument r""" Forward computation for highway layer Parameters ---------- inputs: NDArray The input tensor is of shape `(batch_size, input_size)`. Returns ---------- outputs: NDArray The output tensor is of the same shape with input tensor `(batch_size, input_size)`. """ current_input = inputs for layer in self.hnet: projected_input = layer(current_input) linear_transform = current_input nonlinear_transform, transform_gate = projected_input.split(num_outputs=2, axis=-1) nonlinear_transform = self._activation(nonlinear_transform) transform_gate = transform_gate.sigmoid() current_input = (1 - transform_gate) * linear_transform + \ transform_gate * nonlinear_transform return current_input