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75 lines
3.0 KiB
75 lines
3.0 KiB
#!/usr/bin/env python
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# 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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from paddle.trainer_config_helpers import *
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######################## data source ################################
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dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
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dict_file = dict()
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for line_count, line in enumerate(open(dict_path, "r")):
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dict_file[line.strip()] = line_count
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define_py_data_sources2(train_list='gserver/tests/Sequence/train.list.nest',
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test_list=None,
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module='sequenceGen',
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obj='process2',
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args={"dict_file":dict_file})
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settings(batch_size=2)
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######################## network configure ################################
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dict_dim = len(open(dict_path,'r').readlines())
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word_dim = 128
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hidden_dim = 256
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label_dim = 3
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data = data_layer(name="word", size=dict_dim)
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emb_group = embedding_layer(input=data, size=word_dim)
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# (lstm_input + lstm) is equal to lstmemory
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def lstm_group(lstm_group_input):
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with mixed_layer(size=hidden_dim*4) as group_input:
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group_input += full_matrix_projection(input=lstm_group_input)
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lstm_output = lstmemory_group(input=group_input,
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name="lstm_group",
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size=hidden_dim,
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act=TanhActivation(),
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gate_act=SigmoidActivation(),
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state_act=TanhActivation(),
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lstm_layer_attr=ExtraLayerAttribute(error_clipping_threshold=50))
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return lstm_output
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lstm_nest_group = recurrent_group(input=SubsequenceInput(emb_group),
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step=lstm_group,
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name="lstm_nest_group")
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# hasSubseq ->(seqlastins) seq
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lstm_last = last_seq(input=lstm_nest_group, agg_level=AggregateLevel.EACH_SEQUENCE)
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# seq ->(expand) hasSubseq
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lstm_expand = expand_layer(input=lstm_last, expand_as=emb_group, expand_level=ExpandLevel.FROM_SEQUENCE)
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# hasSubseq ->(average) seq
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lstm_average = pooling_layer(input=lstm_expand,
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pooling_type=AvgPooling(),
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agg_level=AggregateLevel.EACH_SEQUENCE)
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with mixed_layer(size=label_dim,
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act=SoftmaxActivation(),
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bias_attr=True) as output:
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output += full_matrix_projection(input=lstm_average)
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outputs(classification_cost(input=output, label=data_layer(name="label", size=1)))
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