# nnf_normalize: Normalize In torch: Tensors and Neural Networks with 'GPU' Acceleration

## Description

Performs L_p normalization of inputs over specified dimension.

## Usage

 1 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, ε)}.

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

torch documentation built on Oct. 7, 2021, 9:22 a.m.