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

numpy.random.seed(7)

top_words = 5000
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)

max_review_length = 500
X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)

# Create the model
embedding_vector_length = 32
model = Sequential()
model.add(Embedding(top_words, embedding_vector_length, input_length=max_review_length))
model.add(Dropout(0.2))
model.add(LSTM(100))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=3, batch_size=64)

# Evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print('Accuracy: {}'.format(scores[1] * 100))

