# Trains 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

    from keras.preprocessing import sequence
    from keras.models import Sequential
    from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional
    from keras.datasets import imdb

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

    print('Loading data...')
    (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
    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)

    model = Sequential()
    model.add(Embedding(max_features, 128, input_length=maxlen))
    model.add(Bidirectional(LSTM(64)))
    model.add(Dropout(0.5))
    model.add(Dense(1, activation='sigmoid'))

    # 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,
              epochs=4,
              validation_data=[x_test, y_test])

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