tf.raw_ops.FusedBatchNormV2

Batch normalization.

tf.raw_ops.FusedBatchNormV2(
    x,
    scale,
    offset,
    mean,
    variance,
    epsilon=0.0001,
    exponential_avg_factor=1,
    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.

x A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for input data. scale A Tensor. Must be one of the following types: float32. A 1D Tensor for scaling factor, to scale the normalized x. offset A Tensor. Must have the same type as scale. A 1D Tensor for offset, to shift to the normalized x. mean A Tensor. Must have the same type as scale. A 1D Tensor for population mean. Used for inference only; must be empty for training. variance A Tensor. Must have the same type as scale. A 1D Tensor for population variance. Used for inference only; must be empty for training. epsilon An optional float. Defaults to 0.0001. A small float number added to the variance of x. exponential_avg_factor An optional float. Defaults to 1. data_format An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for x and y. 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).