tf.raw_ops.BatchNormWithGlobalNormalizationGrad

Gradients for batch normalization.

tf.raw_ops.BatchNormWithGlobalNormalizationGrad(
    t,
    m,
    v,
    gamma,
    backprop,
    variance_epsilon,
    scale_after_normalization,
    name=None
)

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

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.
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.
backprop A Tensor. Must have the same type as t. 4D backprop 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).

A tuple of Tensor objects (dx, dm, dv, db, dg).
dx A Tensor. Has the same type as t.
dm A Tensor. Has the same type as t.
dv A Tensor. Has the same type as t.
db A Tensor. Has the same type as t.
dg A Tensor. Has the same type as t.