tf.keras.layers.SpectralNormalization

Performs spectral normalization on the weights of a target layer.

Inherits From: Wrapper, Layer, Module

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

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

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.

Examples:

Wrap keras.layers.Conv2D:

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

Wrap keras.layers.Dense:

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

Reference:

Methods

normalize_weights

View source

normalize_weights()

Generate spectral normalized weights.

This method will update the value of self.kernel with the spectral normalized value, so that the layer is ready for call().