Deep learning LSTM example

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

max_features = 10000
maxlen = 50
batch_size = 32

(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, 64))
model.add(LSTM(64, 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)