tf.nn.batch_norm_with_global_normalization

View source on GitHub

Batch normalization.

tf.nn.batch_norm_with_global_normalization(
    input, mean, variance, beta, gamma, variance_epsilon, scale_after_normalization,
    name=None
)

This op is deprecated. See tf.nn.batch_normalization.

Args
input	A 4D input Tensor.
mean	A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof.
variance	A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof.
beta	A 1D beta Tensor with size matching the last dimension of t. An offset to be added to the normalized tensor.
gamma	A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this tensor will be multiplied with the normalized tensor.
variance_epsilon	A small float number to avoid dividing by 0.
scale_after_normalization	A bool indicating whether the resulted tensor needs to be multiplied with gamma.
name	A name for this operation (optional).

Returns
A batch-normalized t.

References:

Batch Normalization - Accelerating Deep Network Training by Reducing Internal Covariate Shift: Ioffe et al., 2015 (pdf)