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92 lines
3.5 KiB
92 lines
3.5 KiB
# edit-mode: -*- 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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"""
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This configuration is a demonstration of how to implement the stacked LSTM
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with residual connections, i.e. an LSTM layer takes the sum of the hidden states
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and inputs of the previous LSTM layer instead of only the hidden states.
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This architecture is from:
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Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi,
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Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey,
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Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser,
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Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens,
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George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa,
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Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean. 2016.
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Google's Neural Machine Translation System: Bridging the Gap between Human and
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Machine Translation. In arXiv https://arxiv.org/pdf/1609.08144v2.pdf
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Different from the architecture described in the paper, we use a stack single
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direction LSTM layers as the first layer instead of bi-directional LSTM. Also,
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since this is a demo code, to reduce computation time, we stacked 4 layers
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instead of 8 layers.
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"""
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from paddle.trainer_config_helpers import *
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dict_file = "./data/dict.txt"
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word_dict = dict()
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with open(dict_file, 'r') as f:
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for i, line in enumerate(f):
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w = line.strip().split()[0]
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word_dict[w] = i
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is_predict = get_config_arg('is_predict', bool, False)
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trn = 'data/train.list' if not is_predict else None
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tst = 'data/test.list' if not is_predict else 'data/pred.list'
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process = 'process' if not is_predict else 'process_predict'
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define_py_data_sources2(
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train_list=trn,
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test_list=tst,
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module="dataprovider_emb",
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obj=process,
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args={"dictionary": word_dict})
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batch_size = 128 if not is_predict else 1
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settings(
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batch_size=batch_size,
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learning_rate=2e-3,
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learning_method=AdamOptimizer(),
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regularization=L2Regularization(8e-4),
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gradient_clipping_threshold=25)
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bias_attr = ParamAttr(initial_std=0., l2_rate=0.)
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data = data_layer(name="word", size=len(word_dict))
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emb = embedding_layer(input=data, size=128)
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lstm = simple_lstm(input=emb, size=128, lstm_cell_attr=ExtraAttr(drop_rate=0.1))
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previous_input, previous_hidden_state = emb, lstm
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for i in range(3):
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# The input to the current layer is the sum of the hidden state
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# and input of the previous layer.
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current_input = addto_layer(input=[previous_input, previous_hidden_state])
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hidden_state = simple_lstm(
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input=current_input, size=128, lstm_cell_attr=ExtraAttr(drop_rate=0.1))
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previous_input, previous_hidden_state = current_input, hidden_state
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lstm = previous_hidden_state
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lstm_last = pooling_layer(input=lstm, pooling_type=MaxPooling())
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output = fc_layer(
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input=lstm_last, size=2, bias_attr=bias_attr, act=SoftmaxActivation())
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if is_predict:
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maxid = maxid_layer(output)
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outputs([maxid, output])
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else:
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label = data_layer(name="label", size=2)
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cls = classification_cost(input=output, label=label)
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outputs(cls)
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