batchnorm.py 文件源码

def normalize(self, x, train=True):
        """
        Returns a batch-normalized version of x.
        """
        if train is not None:
            mean, variance = tf.nn.moments(x, [0, 1, 2])
            assign_mean = self.mean.assign(mean)
            assign_variance = self.variance.assign(variance)
            with tf.control_dependencies([assign_mean, assign_variance]):
                return tf.nn.batch_norm_with_global_normalization(x, mean, variance, self.beta, self.gamma, self.epsilon, self.scale_after_norm)
        else:
            mean = self.ewma_trainer.average(self.mean)
            variance = self.ewma_trainer.average(self.variance)
            local_beta = tf.identity(self.beta)
            local_gamma = tf.identity(self.gamma)
            return tf.nn.batch_norm_with_global_normalization(x, mean, variance, local_beta, local_gamma, self.epsilon, self.scale_after_norm)