torch.nn.utils.parametrizations.spectral_norm(module, name='weight', n_power_iterations=1, eps=1e-12, dim=None)[source]

Applies spectral normalization to a parameter in the given module.

WSN=Wσ(W),σ(W)=maxh:h0Wh2h2\mathbf{W}_{SN} = \dfrac{\mathbf{W}}{\sigma(\mathbf{W})}, \sigma(\mathbf{W}) = \max_{\mathbf{h}: \mathbf{h} \ne 0} \dfrac{\|\mathbf{W} \mathbf{h}\|_2}{\|\mathbf{h}\|_2}

Spectral normalization stabilizes the training of discriminators (critics) in Generative Adversarial Networks (GANs) by rescaling the weight tensor with spectral norm σ\sigma of the weight matrix calculated using power iteration method. If the dimension of the weight tensor is greater than 2, it is reshaped to 2D in power iteration method to get spectral norm.

See Spectral Normalization for Generative Adversarial Networks .

  • module (nn.Module) – containing module

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

  • n_power_iterations (int, optional) – number of power iterations to calculate spectral norm

  • eps (float, optional) – epsilon for numerical stability in calculating norms

  • dim (int, optional) – dimension corresponding to number of outputs, the default is 0, except for modules that are instances of ConvTranspose{1,2,3}d, when it is 1


The original module with a new parametrization registered to the specified weight


This function is implemented using the new parametrization functionality in torch.nn.utils.parametrize.register_parametrization(). It is a reimplementation of torch.nn.utils.spectral_norm().


If the _SpectralNorm module, i.e., module.parametrization.weight[idx], is in training mode on removal, it will perform another power iteration. If you’d like to avoid this iteration, set the module to eval mode before its removal.


>>> snm = spectral_norm(nn.Linear(20, 40))
>>> snm
in_features=20, out_features=40, bias=True
(parametrizations): ModuleDict(
    (weight): ParametrizationList(
    (0): _SpectralNorm()
>>> snm.parametrizations.weight[0].u.size()


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