batch_norm¶

dragon.vm.torch.nn.functional.batch_norm(
  input,
  running_mean,
  running_var,
  weight,
  bias,
  training=False,
  momentum=0.1,
  eps=1e-05
)[source]¶

Apply the batch normalization to input. [Ioffe & Szegedy, 2015].

Parameters:

• input (dragon.vm.torch.Tensor) – The input tensor.
• running_mean (dragon.vm.torch.Tensor) – The running mean.
• running_var (dragon.vm.torch.Tensor) – The running var.
• weight (dragon.vm.torch.Tensor) – The weight tensor.
• bias (dragon.vm.torch.Tensor) – The bias tensor.
• training (bool, optional, default=False) – The flag to determine the stats.
• momentum (float, optional, default=0.1) – The momentum of moving average.
• eps (float, optional, default=1e-5) – The epsilon value.

Returns:

dragon.vm.torch.Tensor – The output tensor.

See also

torch.nn.BatchNorm2d(…)