Function LpNormalization

tinygrad/nn/onnx.py:949–950

(x:Tensor, axis:int=-1, p:int=2)

    return (x - x.mean(axis, keepdim=True)) / (x.std(axis, keepdim=True, correction=0) + 1e-9)

  def LpNormalization(x:Tensor, axis:int=-1, p:int=2):
    return x / (x.abs().sum(axis, keepdim=True) if p == 1 else x.square().sum(axis, keepdim=True).sqrt())

  def OneHot(indices:Tensor, depth:float|int|list[int|float], values:Tensor, axis:int=-1):
    # Scalar or Rank 1 tensor containing exactly one element