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65 lines
2.0 KiB
65 lines
2.0 KiB
7 years ago
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#!/usr/bin/env python
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# 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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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(
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train_list='gserver/tests/Sequence/train.list',
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test_list=None,
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module='sequenceGen',
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obj='process',
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args={"dict_file": dict_file})
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settings(batch_size=5)
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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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sparse_update = get_config_arg("sparse_update", bool, False)
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data = data_layer(name="word", size=dict_dim)
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emb = embedding_layer(
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input=data,
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size=word_dim,
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param_attr=ParamAttr(sparse_update=sparse_update))
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with mixed_layer(size=hidden_dim * 4) as lstm_input:
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lstm_input += full_matrix_projection(input=emb)
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lstm = lstmemory(
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input=lstm_input,
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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_last = last_seq(input=lstm)
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with mixed_layer(
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size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
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output += full_matrix_projection(input=lstm_last)
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outputs(
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classification_cost(
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input=output, label=data_layer(
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name="label", size=1)))
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