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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.
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).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.ValueError
: If mean or variance is not None when is_training is True.