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.

tA 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.
mA 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.
vA 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.
gammaA 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.
backpropA Tensor. Must have the same type as t. 4D backprop Tensor.
variance_epsilonA float. A small float number to avoid dividing by 0.
scale_after_normalizationA bool. A bool indicating whether the resulted tensor needs to be multiplied with gamma.
nameA name for the operation (optional).

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