torch.nn.utils.weight_norm¶

torch.nn.utils.weight_norm(module: T_module, name: str = 'weight', dim: int = 0) → T_module[source]¶

Applies weight normalization to a parameter in the given module.

$$\mathbf{w} = g \dfrac{\mathbf{v}}{\|\mathbf{v}\|}$$

Weight normalization is a reparameterization that decouples the magnitude of a weight tensor from its direction. This replaces the parameter specified by name (e.g. 'weight') with two parameters: one specifying the magnitude (e.g. 'weight_g') and one specifying the direction (e.g. 'weight_v'). Weight normalization is implemented via a hook that recomputes the weight tensor from the magnitude and direction before every forward() call.

By default, with dim=0, the norm is computed independently per output channel/plane. To compute a norm over the entire weight tensor, use dim=None.

See https://arxiv.org/abs/1602.07868

Parameters

• module (Module) – containing module

• name (str, optional) – name of weight parameter

• dim (int, optional) – dimension over which to compute the norm

Returns

The original module with the weight norm hook

Example:

>>> m = weight_norm(nn.Linear(20, 40), name='weight')
>>> m
Linear(in_features=20, out_features=40, bias=True)
>>> m.weight_g.size()
torch.Size([40, 1])
>>> m.weight_v.size()
torch.Size([40, 20])