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20 lines
699 B
Python
20 lines
699 B
Python
from paddle.trainer_config_helpers import *
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def bidirectional_lstm_net(input_dim,
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class_dim=2,
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emb_dim=128,
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lstm_dim=128,
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is_predict=False):
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data = data_layer("word", input_dim)
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emb = embedding_layer(input=data, size=emb_dim)
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bi_lstm = bidirectional_lstm(input=emb, size=lstm_dim)
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dropout = dropout_layer(input=bi_lstm, dropout_rate=0.5)
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output = fc_layer(input=dropout, size=class_dim, act=SoftmaxActivation())
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if not is_predict:
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lbl = data_layer("label", 1)
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outputs(classification_cost(input=output, label=lbl))
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else:
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outputs(output)
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