tf.raw_ops.FusedBatchNormGradV2

Gradient for batch normalization.

tf.raw_ops.FusedBatchNormGradV2(
    y_backprop,
    x,
    scale,
    reserve_space_1,
    reserve_space_2,
    epsilon=0.0001,
    data_format='NHWC',
    is_training=True,
    name=None
)

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).