2016年7月更新 在TensorFlow中使用批处理规范化的最简单方法是通过contrib /
layers，tflearn或slim中提供的高级接口。
如果您想自己动手，则可以使用以前的答案 ：自发布以来，此文档的字符串已得到改进-
请参阅master分支中的docs注释，而不是找到的注释。它特别说明了它是的输出
tf.nn.moments。
您可以在batch_norm测试代码中看到一个非常简单的示例。对于更真实的使用示例，我将其包含在帮助器类下面，并使用了我为自己使用而写的注释（不提供保修！）：
"""A helper class for managing batch normalization state.This class is designed to simplify adding batch normalization (http://arxiv.org/pdf/1502.03167v3.pdf) to your model by managing the state variables associated with it.important use note: The function get_assigner() returns an op that must be executed to save the updated state.A suggested way to do this is to make execution of themodel optimizer force it, e.g., by: update_assignments = tf.group(bn1.get_assigner(), bn2.get_assigner()) with tf.control_dependencies([optimizer]): optimizer = tf.group(update_assignments)"""import tensorflow as tfclass ConvolutionalBatchNormalizer(object): """Helper class that groups the normalization logic and variables. Use: ewma = tf.train.ExponentialMovingAverage(decay=0.99) bn = ConvolutionalBatchNormalizer(depth, 0.001, ewma, True) update_assignments = bn.get_assigner() x = bn.normalize(y, train=training?) (the output x will be batch-normalized). """ def __init__(self, depth, epsilon, ewma_trainer, scale_after_norm): self.mean = tf.Variable(tf.constant(0.0, shape=[depth]), trainable=False) self.variance = tf.Variable(tf.constant(1.0, shape=[depth]), trainable=False) self.beta = tf.Variable(tf.constant(0.0, shape=[depth])) self.gamma = tf.Variable(tf.constant(1.0, shape=[depth])) self.ewma_trainer = ewma_trainer self.epsilon = epsilon self.scale_after_norm = scale_after_norm def get_assigner(self): """Returns an EWMA apply op that must be invoked after optimization.""" return self.ewma_trainer.apply([self.mean, self.variance]) def normalize(self, x, train=True): """Returns a batch-normalized version of x.""" if train: 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)
请注意，我
ConvolutionalBatchNormalizer之所以称其为a是因为它
tf.nn.moments在轴0、1和2上固定了使用sum的用途，而对于非卷积用途，您可能只需要轴0。
如果您使用它，反馈表示赞赏。
