de_noise.py 文件源码

def batch_norm(Wx_plus_b,out_size):
    fc_mean, fc_var = tf.nn.moments(
        Wx_plus_b,
        axes=[0],  # the dimension you wanna normalize, here [0] for batch
        # for image, you wanna do [0, 1, 2] for [batch, height, width] but not channel
    )
    scale = tf.Variable(tf.ones([out_size]))
    shift = tf.Variable(tf.zeros([out_size]))
    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()
    Wx_plus_b = tf.nn.batch_normalization(Wx_plus_b, mean, var, shift, scale, epsilon)
    return Wx_plus_b