nnf_normalize: Normalize

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

Normalize

Description

Performs L_p normalization of inputs over specified dimension.

Usage

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

Arguments

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.

Details

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.

torch documentation built on May 29, 2024, 9:54 a.m.