Source code for gluonnlp.model.convolutional_encoder
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"""Convolutional encoder."""
__all__ = ['ConvolutionalEncoder']
from mxnet import gluon
from mxnet.gluon import nn
from ..initializer import HighwayBias
from .highway import Highway
[docs]class ConvolutionalEncoder(gluon.HybridBlock):
r"""Convolutional encoder.
We implement the convolutional encoder proposed in the following work::
@inproceedings{kim2016character,
title={Character-Aware Neural Language Models.},
author={Kim, Yoon and Jernite, Yacine and Sontag, David and Rush, Alexander M},
booktitle={AAAI},
pages={2741--2749},
year={2016}
}
Parameters
----------
embed_size : int, default 15
The input dimension to the encoder.
We set the default according to the original work's experiments
on PTB dataset with Char-small model setting.
num_filters: Tuple[int], default (25, 50, 75, 100, 125, 150)
The output dimension for each convolutional layer according to the filter sizes,
which are the number of the filters learned by the layers.
We set the default according to the original work's experiments
on PTB dataset with Char-small model setting.
ngram_filter_sizes: Tuple[int], default (1, 2, 3, 4, 5, 6)
The size of each convolutional layer,
and len(ngram_filter_sizes) equals to the number of convolutional layers.
We set the default according to the original work's experiments
on PTB dataset with Char-small model setting.
conv_layer_activation: str, default 'tanh'
Activation function to be used after convolutional layer.
We set the default according to the original work's experiments
on PTB dataset with Char-small model setting.
num_highway: int, default '1'
The number of layers of the Highway layer.
We set the default according to the original work's experiments
on PTB dataset with Char-small model setting.
highway_layer_activation: str, default 'relu'
Activation function to be used after highway layer.
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
We set the default according to the original work's experiments
on PTB dataset with Char-small model setting.
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.
output_size: int, default None
The output dimension after conducting the convolutions and max pooling,
and applying highways, as well as linear projection.
"""
def __init__(self,
embed_size=15,
num_filters=(25, 50, 75, 100, 125, 150),
ngram_filter_sizes=(1, 2, 3, 4, 5, 6),
conv_layer_activation='tanh',
num_highway=1,
highway_layer_activation='relu',
highway_bias=HighwayBias(nonlinear_transform_bias=0.0, transform_gate_bias=-2.0),
output_size=None,
**kwargs):
super(ConvolutionalEncoder, self).__init__(**kwargs)
self._embed_size = embed_size
self._num_filters = num_filters
self._ngram_filter_sizes = ngram_filter_sizes
self._num_highway = num_highway
self._output_size = output_size
with self.name_scope():
self._convs = gluon.contrib.nn.HybridConcurrent()
maxpool_output_size = 0
with self._convs.name_scope():
for num_filter, ngram_size in zip(self._num_filters, self._ngram_filter_sizes):
seq = nn.HybridSequential()
seq.add(nn.Conv1D(in_channels=self._embed_size,
channels=num_filter,
kernel_size=ngram_size,
use_bias=True))
seq.add(gluon.nn.HybridLambda(lambda F, x: F.max(x, axis=2)))
if conv_layer_activation is not None:
seq.add(nn.Activation(conv_layer_activation))
self._convs.add(seq)
maxpool_output_size += num_filter
if self._num_highway:
self._highways = Highway(maxpool_output_size,
self._num_highway,
activation=highway_layer_activation,
highway_bias=highway_bias)
else:
self._highways = None
if self._output_size:
self._projection = nn.Dense(in_units=maxpool_output_size,
units=self._output_size,
use_bias=True)
else:
self._projection = None
self._output_size = maxpool_output_size
[docs] def hybrid_forward(self, F, inputs, mask=None): # pylint: disable=arguments-differ
r"""
Forward computation for char_encoder
Parameters
----------
inputs: NDArray
The input tensor is of shape `(seq_len, batch_size, embedding_size)` TNC.
mask: NDArray
The mask applied to the input of shape `(seq_len, batch_size)`, the mask will
be broadcasted along the embedding dimension.
Returns
----------
output: NDArray
The output of the encoder with shape `(batch_size, output_size)`
"""
if mask is not None:
inputs = F.broadcast_mul(inputs, mask.expand_dims(-1))
inputs = F.transpose(inputs, axes=(1, 2, 0))
output = self._convs(inputs)
if self._highways:
output = self._highways(output)
if self._projection:
output = self._projection(output)
return output