from __future__ import print_function
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Embedding
from keras.layers import LSTM
from keras.datasets import imdb
batch_size = 32
max_features = 20000
maxlen = 80 # cut texts after this number of words (among top max_features most common words)
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=max_features)
X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
model = Sequential()
model.add(Embedding(max_features, 128))
model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam',metrics='accuracy')
model.fit(X_train, y_train, batch_size=batch_size, epochs=15, validation_data=(X_test, y_test))
score, acc = model.evaluate(X_test, y_test,batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)
