torch.nn.functional.normalize

torch.nn.functional.normalize(input, p=2.0, dim=1, eps=1e-12, out=None)

Perform $L_p$ normalization of inputs over specified dimension.

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

$$v = \frac{v}{\max(\lVert v \rVert_p, \epsilon)}.$$

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

Parameters

• input (Tensor) – input tensor of any shape

• p (float) – the exponent value in the norm formulation. Default: 2

• dim (int or tuple of ints) – 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.

Return type

Tensor