122 lines
4.3 KiB
Python
122 lines
4.3 KiB
Python
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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The function lex_net(args) define the lexical analysis network structure
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"""
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import sys
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import os
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import math
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import paddle.fluid as fluid
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from paddle.fluid.initializer import NormalInitializer
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def lex_net(word, vocab_size, num_labels, for_infer=True, target=None):
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"""
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define the lexical analysis network structure
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word: stores the input of the model
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for_infer: a boolean value, indicating if the model to be created is for training or predicting.
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return:
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for infer: return the prediction
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otherwise: return the prediction
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"""
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word_emb_dim=128
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grnn_hidden_dim=128
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bigru_num=2
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emb_lr = 1.0
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crf_lr = 1.0
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init_bound = 0.1
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IS_SPARSE = True
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def _bigru_layer(input_feature):
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"""
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define the bidirectional gru layer
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"""
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pre_gru = fluid.layers.fc(
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input=input_feature,
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size=grnn_hidden_dim * 3,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Uniform(
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low=-init_bound, high=init_bound),
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=1e-4)))
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gru = fluid.layers.dynamic_gru(
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input=pre_gru,
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size=grnn_hidden_dim,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Uniform(
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low=-init_bound, high=init_bound),
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=1e-4)))
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pre_gru_r = fluid.layers.fc(
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input=input_feature,
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size=grnn_hidden_dim * 3,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Uniform(
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low=-init_bound, high=init_bound),
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=1e-4)))
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gru_r = fluid.layers.dynamic_gru(
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input=pre_gru_r,
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size=grnn_hidden_dim,
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is_reverse=True,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Uniform(
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low=-init_bound, high=init_bound),
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=1e-4)))
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bi_merge = fluid.layers.concat(input=[gru, gru_r], axis=1)
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return bi_merge
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def _net_conf(word, target=None):
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"""
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Configure the network
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"""
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word_embedding = fluid.embedding(
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input=word,
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size=[vocab_size, word_emb_dim],
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dtype='float32',
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is_sparse=IS_SPARSE,
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param_attr=fluid.ParamAttr(
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learning_rate=emb_lr,
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name="word_emb",
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initializer=fluid.initializer.Uniform(
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low=-init_bound, high=init_bound)))
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input_feature = word_embedding
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for i in range(bigru_num):
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bigru_output = _bigru_layer(input_feature)
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input_feature = bigru_output
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emission = fluid.layers.fc(
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size=num_labels,
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input=bigru_output,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Uniform(
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low=-init_bound, high=init_bound),
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=1e-4)))
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size = emission.shape[1]
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fluid.layers.create_parameter(
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shape=[size + 2, size], dtype=emission.dtype, name='crfw')
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crf_decode = fluid.layers.crf_decoding(
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input=emission, param_attr=fluid.ParamAttr(name='crfw'))
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return crf_decode
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return _net_conf(word)
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