Source code for gluonnlp.model.convolutional_encoder

# coding: utf-8

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"""Convolutional encoder."""

from __future__ import absolute_import
from __future__ import print_function

__all__ = ['ConvolutionalEncoder']

from mxnet import gluon
from mxnet.gluon import nn
from gluonnlp.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