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Batch normalization.
tf.compat.v1.nn.batch_norm_with_global_normalization(
t=None, m=None, v=None, beta=None, gamma=None, variance_epsilon=None,
scale_after_normalization=None, name=None, input=None, mean=None, variance=None
)
This op is deprecated. See tf.nn.batch_normalization.
Args | |
|---|---|
t
|
A 4D input Tensor. |
m
|
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. |
v
|
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). |
input
|
Alias for t. |
mean
|
Alias for m. |
variance
|
Alias for v. |
Returns | |
|---|---|
A batch-normalized t.
|
References:
Batch Normalization - Accelerating Deep Network Training by Reducing Internal Covariate Shift: Ioffe et al., 2015 (pdf)