[keras][tutorial] custom loss using closure

# https://github.com/keras-team/keras/issues/2121#issuecomment-214551349
def penalized_loss(noise):
    def loss(y_true, y_pred):
        return K.mean(K.square(y_pred - y_true) - K.square(y_true - noise), axis=-1)
    return loss


input1 = Input(batch_shape=(batch_size, timesteps, features))
lstm =  LSTM(features, stateful=True, return_sequences=True)(input1)
output1 = TimeDistributed(Dense(features, activation='sigmoid'))(lstm)
output2 = TimeDistributed(Dense(features, activation='sigmoid'))(lstm)
model = Model(input=[input1], output=[output1, output2])
model.compile(loss=[penalized_loss(noise=output2), penalized_loss(noise=output1)], optimizer='rmsprop')