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