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219 lines
6.3 KiB
219 lines
6.3 KiB
# Copyright (c) 2016 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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import math
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import os
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import sys
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from paddle.trainer_config_helpers import *
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#file paths
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word_dict_file = './data/wordDict.txt'
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label_dict_file = './data/targetDict.txt'
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predicate_file = './data/verbDict.txt'
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train_list_file = './data/train.list'
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test_list_file = './data/test.list'
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is_test = get_config_arg('is_test', bool, False)
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is_predict = get_config_arg('is_predict', bool, False)
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if not is_predict:
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#load dictionaries
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word_dict = dict()
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label_dict = dict()
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predicate_dict = dict()
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with open(word_dict_file, 'r') as f_word, \
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open(label_dict_file, 'r') as f_label, \
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open(predicate_file, 'r') as f_pre:
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for i, line in enumerate(f_word):
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w = line.strip()
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word_dict[w] = i
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for i, line in enumerate(f_label):
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w = line.strip()
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label_dict[w] = i
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for i, line in enumerate(f_pre):
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w = line.strip()
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predicate_dict[w] = i
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if is_test:
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train_list_file = None
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#define data provider
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define_py_data_sources2(
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train_list=train_list_file,
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test_list=test_list_file,
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module='dataprovider',
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obj='process',
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args={
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'word_dict': word_dict,
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'label_dict': label_dict,
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'predicate_dict': predicate_dict
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})
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word_dict_len = len(word_dict)
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label_dict_len = len(label_dict)
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pred_len = len(predicate_dict)
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else:
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word_dict_len = get_config_arg('dict_len', int)
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label_dict_len = get_config_arg('label_len', int)
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pred_len = get_config_arg('pred_len', int)
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############################## Hyper-parameters ##################################
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mark_dict_len = 2
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word_dim = 32
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mark_dim = 5
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hidden_dim = 512
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depth = 8
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########################### Optimizer #######################################
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settings(
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batch_size=150,
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learning_method=MomentumOptimizer(momentum=0),
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learning_rate=2e-2,
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regularization=L2Regularization(8e-4),
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is_async=False,
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model_average=ModelAverage(
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average_window=0.5, max_average_window=10000), )
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####################################### network ##############################
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#8 features and 1 target
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word = data_layer(name='word_data', size=word_dict_len)
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predicate = data_layer(name='verb_data', size=pred_len)
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ctx_n2 = data_layer(name='ctx_n2_data', size=word_dict_len)
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ctx_n1 = data_layer(name='ctx_n1_data', size=word_dict_len)
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ctx_0 = data_layer(name='ctx_0_data', size=word_dict_len)
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ctx_p1 = data_layer(name='ctx_p1_data', size=word_dict_len)
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ctx_p2 = data_layer(name='ctx_p2_data', size=word_dict_len)
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mark = data_layer(name='mark_data', size=mark_dict_len)
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if not is_predict:
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target = data_layer(name='target', size=label_dict_len)
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default_std = 1 / math.sqrt(hidden_dim) / 3.0
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emb_para = ParameterAttribute(name='emb', initial_std=0., learning_rate=0.)
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std_0 = ParameterAttribute(initial_std=0.)
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std_default = ParameterAttribute(initial_std=default_std)
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predicate_embedding = embedding_layer(
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size=word_dim,
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input=predicate,
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param_attr=ParameterAttribute(
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name='vemb', initial_std=default_std))
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mark_embedding = embedding_layer(
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name='word_ctx-in_embedding', size=mark_dim, input=mark, param_attr=std_0)
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word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
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emb_layers = [
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embedding_layer(
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size=word_dim, input=x, param_attr=emb_para) for x in word_input
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]
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emb_layers.append(predicate_embedding)
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emb_layers.append(mark_embedding)
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hidden_0 = mixed_layer(
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name='hidden0',
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size=hidden_dim,
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bias_attr=std_default,
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input=[
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full_matrix_projection(
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input=emb, param_attr=std_default) for emb in emb_layers
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])
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mix_hidden_lr = 1e-3
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lstm_para_attr = ParameterAttribute(initial_std=0.0, learning_rate=1.0)
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hidden_para_attr = ParameterAttribute(
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initial_std=default_std, learning_rate=mix_hidden_lr)
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lstm_0 = lstmemory(
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name='lstm0',
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input=hidden_0,
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act=ReluActivation(),
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gate_act=SigmoidActivation(),
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state_act=SigmoidActivation(),
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bias_attr=std_0,
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param_attr=lstm_para_attr)
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#stack L-LSTM and R-LSTM with direct edges
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input_tmp = [hidden_0, lstm_0]
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for i in range(1, depth):
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mix_hidden = mixed_layer(
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name='hidden' + str(i),
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size=hidden_dim,
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bias_attr=std_default,
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input=[
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full_matrix_projection(
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input=input_tmp[0], param_attr=hidden_para_attr),
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full_matrix_projection(
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input=input_tmp[1], param_attr=lstm_para_attr)
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])
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lstm = lstmemory(
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name='lstm' + str(i),
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input=mix_hidden,
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act=ReluActivation(),
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gate_act=SigmoidActivation(),
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state_act=SigmoidActivation(),
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reverse=((i % 2) == 1),
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bias_attr=std_0,
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param_attr=lstm_para_attr)
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input_tmp = [mix_hidden, lstm]
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feature_out = mixed_layer(
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name='output',
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size=label_dict_len,
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bias_attr=std_default,
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input=[
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full_matrix_projection(
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input=input_tmp[0], param_attr=hidden_para_attr),
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full_matrix_projection(
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input=input_tmp[1], param_attr=lstm_para_attr)
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], )
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if not is_predict:
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crf_l = crf_layer(
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name='crf',
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size=label_dict_len,
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input=feature_out,
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label=target,
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param_attr=ParameterAttribute(
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name='crfw', initial_std=default_std, learning_rate=mix_hidden_lr))
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crf_dec_l = crf_decoding_layer(
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name='crf_dec_l',
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size=label_dict_len,
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input=feature_out,
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label=target,
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param_attr=ParameterAttribute(name='crfw'))
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eval = sum_evaluator(input=crf_dec_l)
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outputs(crf_l)
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else:
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crf_dec_l = crf_decoding_layer(
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name='crf_dec_l',
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size=label_dict_len,
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input=feature_out,
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param_attr=ParameterAttribute(name='crfw'))
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outputs(crf_dec_l)
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