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142 lines
4.2 KiB
142 lines
4.2 KiB
# Copyright (c) 2016 Baidu, Inc. 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/src.dict'
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label_dict_file = './data/tgt.dict'
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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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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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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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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={'word_dict': word_dict,
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'label_dict': label_dict})
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word_dict_len = len(word_dict)
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label_dict_len = len(label_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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mark_dict_len = 2
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word_dim = 32
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mark_dim = 5
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hidden_dim = 128
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depth = 8
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emb_lr = 1e-2
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fc_lr = 1e-2
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lstm_lr = 2e-2
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settings(
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batch_size=150,
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learning_method=AdamOptimizer(),
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learning_rate=1e-3,
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regularization=L2Regularization(8e-4),
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gradient_clipping_threshold=25)
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#6 features
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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=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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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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ptt = ParameterAttribute(name='src_emb', learning_rate=emb_lr)
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layer_attr = ExtraLayerAttribute(drop_rate=0.5)
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fc_para_attr = ParameterAttribute(learning_rate=fc_lr)
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lstm_para_attr = ParameterAttribute(initial_std=0., learning_rate=lstm_lr)
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para_attr = [fc_para_attr, lstm_para_attr]
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word_embedding = embedding_layer(size=word_dim, input=word, param_attr=ptt)
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predicate_embedding = embedding_layer(
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size=word_dim, input=predicate, param_attr=ptt)
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ctx_n1_embedding = embedding_layer(size=word_dim, input=ctx_n1, param_attr=ptt)
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ctx_0_embedding = embedding_layer(size=word_dim, input=ctx_0, param_attr=ptt)
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ctx_p1_embedding = embedding_layer(size=word_dim, input=ctx_p1, param_attr=ptt)
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mark_embedding = embedding_layer(size=mark_dim, input=mark)
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hidden_0 = mixed_layer(
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size=hidden_dim,
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input=[
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full_matrix_projection(input=word_embedding),
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full_matrix_projection(input=predicate_embedding),
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full_matrix_projection(input=ctx_n1_embedding),
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full_matrix_projection(input=ctx_0_embedding),
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full_matrix_projection(input=ctx_p1_embedding),
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full_matrix_projection(input=mark_embedding),
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])
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lstm_0 = lstmemory(input=hidden_0, layer_attr=layer_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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fc = fc_layer(input=input_tmp, size=hidden_dim, param_attr=para_attr)
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lstm = lstmemory(
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input=fc,
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act=ReluActivation(),
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reverse=(i % 2) == 1,
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layer_attr=layer_attr)
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input_tmp = [fc, lstm]
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prob = fc_layer(
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input=input_tmp,
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size=label_dict_len,
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act=SoftmaxActivation(),
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param_attr=para_attr)
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if not is_predict:
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cls = classification_cost(input=prob, label=target)
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outputs(cls)
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else:
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outputs(prob)
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