tf.compat.v1.nn.batch_norm_with_global_normalization
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)
|
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Last updated 2024-04-26 UTC.
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