tf.raw_ops.FusedBatchNormGradV2

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Gradient for batch normalization.

Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.

y_backprop A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for the gradient with respect to y.
x A Tensor. Must have the same type as y_backprop. A 4D Tensor for input data.
scale A Tensor of type float32. A 1D Tensor for scaling factor, to scale the normalized x.
reserve_space_1 A Tensor. Must be one of the following types: float32. When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation.
reserve_space_2 A Tensor. Must have the same type as reserve_space_1. When is_training is True, a 1D Tensor for the computed batch variance (inverted variance in the cuDNN case) to be reused in gradient computation. When is_training is False, a 1D Tensor for the population variance to be reused in both 1st and 2nd order gradient computation.
epsilon An optional float. Defaults to 0.0001. A small float number added to the variance of x.
data_format An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW".
is_training An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.
name A name for the operation (optional).

A tuple of Tensor objects (x_backprop, scale_backprop, offset_backprop, reserve_space_3, reserve_space_4).
x_backprop A Tensor. Has the same type as y_backprop.
scale_backprop A Tensor. Has the same type as reserve_space_1.
offset_backprop A Tensor. Has the same type as reserve_space_1.
reserve_space_3 A Tensor. Has the same type as reserve_space_1.
reserve_space_4 A Tensor. Has the same type as reserve_space_1.