Spectral Normalization layer

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SpectralNormalization class

keras.layers.SpectralNormalization(layer, power_iterations=1, **kwargs) 

Performs spectral normalization on the weights of a target layer.

This wrapper controls the Lipschitz constant of the weights of a layer by constraining their spectral norm, which can stabilize the training of GANs.

Arguments

• layer: A keras.layers.Layer instance that has either a kernel (e.g. Conv2D, Dense...) or an embeddings attribute (Embedding layer).
• power_iterations: int, the number of iterations during normalization.
• **kwargs: Base wrapper keyword arguments.

Examples

Wrap keras.layers.Conv2D:

>>> x = np.random.rand(1, 10, 10, 1) >>> conv2d = SpectralNormalization(keras.layers.Conv2D(2, 2)) >>> y = conv2d(x) >>> y.shape (1, 9, 9, 2) 

Wrap keras.layers.Dense:

>>> x = np.random.rand(1, 10, 10, 1) >>> dense = SpectralNormalization(keras.layers.Dense(10)) >>> y = dense(x) >>> y.shape (1, 10, 10, 10) 

Reference

• Spectral Normalization for GAN.