imdb_bidirectional_lstm.py

'''Train a Bidirectional LSTM on the IMDB sentiment classification task.
Output after 4 epochs on CPU: ~0.8146
Time per epoch on CPU (Core i7): ~150s.
'''

from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility

from keras.preprocessing import sequence
from keras.models import Model
from keras.layers import Dense, Dropout, Embedding, LSTM, Input, merge
from keras.datasets import imdb


max_features = 20000
maxlen = 100  # cut texts after this number of words (among top max_features most common words)
batch_size = 32

print('Loading data...')
(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features,
                                                      test_split=0.2)
print(len(X_train), 'train sequences')
print(len(X_test), 'test sequences')

print("Pad sequences (samples x time)")
X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)
y_train = np.array(y_train)
y_test = np.array(y_test)


# this is the placeholder tensor for the input sequences
sequence = Input(shape=(maxlen,), dtype='int32')
# this embedding layer will transform the sequences of integers
# into vectors of size 128
embedded = Embedding(max_features, 1000, input_length=maxlen)(sequence)

# apply forwards LSTM
forwards = LSTM(64, dropout_W=0.5)(embedded)
# apply backwards LSTM
backwards = LSTM(64, go_backwards=True)(embedded)

# concatenate the outputs of the 2 LSTMs
merged = merge([forwards, backwards], mode='concat', concat_axis=-1)
after_dp = Dropout(0.5)(merged)
output = Dense(1, activation='sigmoid')(after_dp)

model = Model(input=sequence, output=output)

# try using different optimizers and different optimizer configs
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])

print('Train...')
model.fit(X_train, y_train,
          batch_size=batch_size,
          nb_epoch=4,
          validation_data=[X_test, y_test])