de_noise.py 文件源码

def input_norm(xs):
    fc_mean, fc_var = tf.nn.moments(
        xs,
        axes=[0],
    )
    scale = tf.Variable(tf.ones([1]))
    shift = tf.Variable(tf.zeros([1]))
    epsilon = 0.001
    # apply moving average for mean and var when train on batch
    ema = tf.train.ExponentialMovingAverage(decay=0.5)

    def mean_var_with_update():
        ema_apply_op = ema.apply([fc_mean, fc_var])
        with tf.control_dependencies([ema_apply_op]):
            return tf.identity(fc_mean), tf.identity(fc_var)

    mean, var = mean_var_with_update()
    xs = tf.nn.batch_normalization(xs, mean, var, shift, scale, epsilon)
    return xs