model.py 文件源码

def huber_loss(x, delta=1):
    coef = 0.5
    l2_mask = tf.less_equal(tf.abs(x), delta)
    l1_mask = tf.greater(tf.abs(x), delta)

    term_1 = tf.reduce_sum(coef * tf.square(tf.boolean_mask(x, l2_mask)))
    term_2 = tf.reduce_sum(delta * (tf.abs(tf.boolean_mask(x, l1_mask)) - coef * delta))

    return term_1 + term_2