tf.compat.v1.nn.fused_batch_norm

Batch normalization.

tf.compat.v1.nn.fused_batch_norm(
    x, scale, offset, mean=None, variance=None, epsilon=0.001, data_format='NHWC',
    is_training=True, name=None
)

See Source: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy.

Args
`x`	Input `Tensor` of 4 dimensions.
`scale`	A `Tensor` of 1 dimension for scaling.
`offset`	A `Tensor` of 1 dimension for bias.
`mean`	A `Tensor` of 1 dimension for population mean used for inference.
`variance`	A `Tensor` of 1 dimension for population variance used for inference.
`epsilon`	A small float number added to the variance of x.
`data_format`	The data format for x. Either "NHWC" (default) or "NCHW".
`is_training`	A bool value to specify if the operation is used for training or inference.
`name`	A name for this operation (optional).

Returns
`y`	A 4D Tensor for the normalized, scaled, offsetted x.
`batch_mean`	A 1D Tensor for the mean of x.
`batch_var`	A 1D Tensor for the variance of x.

Raises
`ValueError`	If mean or variance is not None when is_training is True.

tf.compat.v1.nn.fused_batch_norm

Args

Returns

Raises