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Batch normalization.

    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.