norm

dragon.vm.torch.norm(
  input,
  p='fro',
  dim=None,
  keepdim=False,
  out=None,
  dtype=None
)[source]

Compute the norm value of elements along the given dimension.

dim could be negative or None:

x = torch.tensor([[1., 2., 3.], [4., 5., 6.]])

# A negative dimension is the last-k axis
print(torch.norm(x, dim=1))
print(torch.norm(x, dim=-1))  # Equivalent

# If ``dim`` is None, the vector-style reduction
# will be applied to return a scalar result
print(torch.norm(x))  # 9.539

# Also, ``dim`` could be a sequence of integers
print(torch.norm(x, dim=(0, 1)))  # 9.539
Parameters:
  • input (dragon.vm.torch.Tensor) – The input tensor.
  • p ({'fro', 1, 2}, optional) – The norm order.
  • dim (Union[int, Sequence[int]], optional) – The dimension to reduce.
  • keepdim (bool, optional, default=False) – Keep the reduced dimension or not.
  • out (dragon.vm.torch.Tensor, optional) – The output tensor.
  • dtype (str, optional) – The data type to cast to.
Returns:

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