Batch normalization.
tf.raw_ops.BatchNormWithGlobalNormalization(
t,
m,
v,
beta,
gamma,
variance_epsilon,
scale_after_normalization,
name=None
)
This op is deprecated. Prefer tf.nn.batch_normalization
.
Args |
t
|
A Tensor . Must be one of the following types: float32 , float64 , int32 , uint8 , int16 , int8 , complex64 , int64 , qint8 , quint8 , qint32 , bfloat16 , qint16 , quint16 , uint16 , complex128 , half , uint32 , uint64 .
A 4D input Tensor.
|
m
|
A Tensor . Must have the same type as t .
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 Tensor . Must have the same type as t .
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 Tensor . Must have the same type as t .
A 1D beta Tensor with size matching the last dimension of t.
An offset to be added to the normalized tensor.
|
gamma
|
A Tensor . Must have the same type as t .
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 float . A small float number to avoid dividing by 0.
|
scale_after_normalization
|
A bool .
A bool indicating whether the resulted tensor
needs to be multiplied with gamma.
|
name
|
A name for the operation (optional).
|
Returns |
A Tensor . Has the same type as t .
|