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

 torch_norm R Documentation

Norm

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.