Normalize — nnf_normalize • torch

Performs $L_p$ normalization of inputs over specified dimension.

Usage

nnf_normalize(input, p = 2, dim = 2, eps = 1e-12, out = NULL)

input: input tensor of any shape
p: (float) the exponent value in the norm formulation. Default: 2
dim: (int) the dimension to reduce. Default: 1
eps: (float) small value to avoid division by zero. Default: 1e-12
out: (Tensor, optional) the output tensor. If out is used, this operation won't be differentiable.

For a tensor input of sizes $(n_0, ..., n_{dim}, ..., n_k)$, each $n_{dim}$ -element vector $v$ along dimension dim is transformed as

$$ v = \frac{v}{\max(\Vert v \Vert_p, \epsilon)}. $$

With the default arguments it uses the Euclidean norm over vectors along dimension $1$ for normalization.