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Fix use of dropout in sentiment analysis LSTM example

This commit is contained in:
Matthew Honnibal 2016-12-20 16:26:38 -06:00
parent 1ed40682a3
commit 7793e2ad82
1 changed files with 4 additions and 5 deletions
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9
examples/deep_learning_keras.py
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@ -111,10 +111,9 @@ def compile_lstm(embeddings, shape, settings):
mask_zero=True
)
)
model.add(TimeDistributed(Dense(shape['nr_hidden'] * 2, bias=False)))
model.add(Dropout(settings['dropout']))
model.add(Bidirectional(LSTM(shape['nr_hidden'])))
model.add(Dropout(settings['dropout']))
model.add(TimeDistributed(Dense(shape['nr_hidden'], bias=False)))
model.add(Bidirectional(LSTM(shape['nr_hidden'], dropout_U=settings['dropout'],
dropout_W=settings['dropout'])))
model.add(Dense(shape['nr_class'], activation='sigmoid'))
model.compile(optimizer=Adam(lr=settings['lr']), loss='binary_crossentropy',
metrics=['accuracy'])
@ -195,7 +194,7 @@ def main(model_dir, train_dir, dev_dir,
dev_labels = numpy.asarray(dev_labels, dtype='int32')
lstm = train(train_texts, train_labels, dev_texts, dev_labels,
{'nr_hidden': nr_hidden, 'max_length': max_length, 'nr_class': 1},
{'dropout': 0.5, 'lr': learn_rate},
{'dropout': dropout, 'lr': learn_rate},
{},
nb_epoch=nb_epoch, batch_size=batch_size)
weights = lstm.get_weights()