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tf.nn.fused_batch_norm

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Batch normalization.

Aliases:

tf.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.