# create the model
model = Sequential()
model.add(Embedding(top_words, 32, input_length=max_words))
model.add(Flatten())
model.add(Dense(250, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=128, verbose=2)
# save the model
model_json = model.to_json()
jsonFile = join(dir, 'sentiment.' + str(output_dim) + '.json')
weightsFile = join(dir, 'sentiment_weights.' + str(output_dim) + '.h5')
model.save_weights(weightsFile)