chainer.functions.normalize¶

chainer.functions.normalize(x, eps=1e-05, axis=1)[source]¶

L2 norm squared (a.k.a. Euclidean norm).

This function implements L2 normalization on a vector along the given axis. No reduction is done along the normalization axis.

In the case when axis=1 and $\mathbf{x}$ is a matrix of dimension $(N, K)$ , where $N$ and $K$ denote mini-batch size and the dimension of the input vectors, this function computes an output matrix $\mathbf{y}$ of dimension $(N, K)$ by the following equation:

$\mathbf{y}_i = {\mathbf{x}_i \over \| \mathbf{x}_i \|_2 + \epsilon}$

eps is used to avoid division by zero when norm of $\mathbf{x}$ along the given axis is zero.

The default value of axis is determined for backward compatibility.

Parameters

x (Variable or N-dimensional array) – Two dimensional output variable. The first dimension is assumed to be the mini-batch dimension.
eps (float) – Epsilon value for numerical stability.
axis (int or tuple of ints) – Axis along which to normalize.

Returns

The output variable which has the same shape as $x$ .

Return type

Variable