小编wid*_*txp的帖子

我在tensorflow中尝试了几个版本的batch_normalization,但它们都没有工作!当我在推理时设置batch_size = 1时,结果都是错误的.

版本1:直接使用tensorflow.contrib中的官方版本

from tensorflow.contrib.layers.python.layers.layers import batch_norm

使用这样:

output = lrelu(batch_norm(tf.nn.bias_add(conv, biases), is_training), 0.5, name=scope.name)

is_training =训练时为真,推理时为假.

版本2:如何在TensorFlow中使用批量标准化？

def batch_norm_layer(x, train_phase, scope_bn='bn'):
    bn_train = batch_norm(x, decay=0.999, epsilon=1e-3, center=True, scale=True,
            updates_collections=None,
            is_training=True,
            reuse=None, # is this right?
            trainable=True,
            scope=scope_bn)
    bn_inference = batch_norm(x, decay=0.999, epsilon=1e-3, center=True, scale=True,
            updates_collections=None,
            is_training=False,
            reuse=True, # is this right?
            trainable=True,
            scope=scope_bn)
    z = tf.cond(train_phase, lambda: bn_train, lambda: bn_inference)
    return z

使用这样:

output = lrelu(batch_norm_layer(tf.nn.bias_add(conv, biases), is_training), 0.5, name=scope.name)

is_training是培训时间的占位符,在推理时间是真假.

版本3:来自slim https://github.com/tensorflow/models/blob/master/inception/inception/slim/ops.py

def batch_norm_layer(inputs,
           is_training=True,
           scope='bn'): …