model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
# model.compile(loss='binary_crossentropy', optimizer='rmsprop', class_mode='binary')
model_b = Sequential()
model_b.add(Dense(287, init='uniform', input_shape=(sequence_length,)))
model_b.add(Dense(32, init='uniform'))
model_b.add(Activation('relu'))
model_b.add(Dense(2, init='uniform'))
model_b.compile(loss='binary_crossentropy', optimizer='rmsprop', class_mode='binary')
decoder = Sequential()
decoder.add(Merge([model, model_b], mode='concat'))
decoder.add(Dense(2, activation='softmax'))
decoder.compile(loss='binary_crossentropy', optimizer='rmsprop', class_mode='binary')
# Training model
# ==================================================
print ("Drawing graph")
graph = to_graph(decoder, show_shape=True)
graph.write_png("model.png")
print ("Training model")
decoder.fit([x_shuffled, x_pos], y_shuffled, batch_size=batch_size,
nb_epoch=num_epochs, show_accuracy=True,
validation_split=val_split, verbose=2)
