torch (version 0.0.2)

torch_norm: Norm

Description

Norm

Arguments

input

(Tensor) the input tensor

p

(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: 'fro' The following norms can be calculated: ===== ============================ ========================== ord matrix norm vector norm ===== ============================ ========================== None Frobenius norm 2-norm 'fro' Frobenius norm -- 'nuc' nuclear norm -- Other as vec norm when dim is None sum(abs(x)ord)(1./ord) ===== ============================ ==========================

dim

(int, 2-tuple of ints, 2-list of ints, optional) If it is an int, vector norm will be calculated, if it is 2-tuple of ints, matrix norm will be calculated. If the value is None, matrix norm will be calculated when the input tensor only has two dimensions, vector norm will be calculated when the input tensor only has one dimension. If the input tensor has more than two dimensions, the vector norm will be applied to last dimension.

keepdim

(bool, optional) whether the output tensors have dim retained or not. Ignored if dim = None and out = None. Default: False

out

(Tensor, optional) the output tensor. Ignored if dim = None and out = None.

dtype

(torch.dtype, optional) the desired data type of returned tensor. If specified, the input tensor is casted to 'dtype' while performing the operation. Default: None.

TEST

Returns the matrix norm or vector norm of a given tensor.

Examples

# NOT RUN {
if (torch_is_installed()) {

a = torch_arange(0, 9, dtype = torch_float())
b = a$reshape(list(3, 3))
torch_norm(a)
torch_norm(b)
torch_norm(a, Inf)
torch_norm(b, Inf)

}
# }