torch_norm: Norm

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Norm

Description

Norm

Usage

torch_norm(self, p = 2L, dim, keepdim = FALSE, dtype)

Arguments

self	(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 ===== ============================ ========================== NULL Frobenius norm 2-norm 'fro' Frobenius norm – 'nuc' nuclear norm – Other as vec norm when dim is NULL 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 NULL, 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 = NULL and out = NULL. Default: FALSE Ignored if dim = NULL and out = NULL.
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: NULL.

TEST

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

Examples

if (torch_is_installed()) {

a <- torch_arange(1, 9, dtype = torch_float())
b <- a$reshape(list(3, 3))
torch_norm(a)
torch_norm(b)
torch_norm(a, Inf)
torch_norm(b, Inf)

}

torch documentation built on June 7, 2023, 6:19 p.m.