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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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