webzjuyujun
49 linhas
1.4 KiB
Python
49 linhas
1.4 KiB
Python
'''Train a Bidirectional LSTM on the IMDB sentiment classification task.
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Output after 4 epochs on CPU: ~0.8146
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Time per epoch on CPU (Core i7): ~150s.
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'''
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from __future__ import print_function
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import numpy as np
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from keras.preprocessing import sequence
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional
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from keras.datasets import imdb
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max_features = 20000
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# cut texts after this number of words
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# (among top max_features most common words)
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maxlen = 100
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batch_size = 32
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print('Loading data...')
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(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
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print(len(x_train), 'train sequences')
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print(len(x_test), 'test sequences')
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print('Pad sequences (samples x time)')
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x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
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x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
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print('x_train shape:', x_train.shape)
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print('x_test shape:', x_test.shape)
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y_train = np.array(y_train)
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y_test = np.array(y_test)
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model = Sequential()
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model.add(Embedding(max_features, 128, input_length=maxlen))
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model.add(Bidirectional(LSTM(64)))
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model.add(Dropout(0.5))
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model.add(Dense(1, activation='sigmoid'))
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# try using different optimizers and different optimizer configs
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model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])
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print('Train...')
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=4,
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validation_data=[x_test, y_test])
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