tf.keras.layers.SpectralNormalization | TensorFlow v2.16.1 (original) (raw)
tf.keras.layers.SpectralNormalization
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Performs spectral normalization on the weights of a target layer.
Inherits From: Wrapper, Layer, Operation
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.
| Args | |
|---|---|
| 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:
| Attributes | |
|---|---|
| input | Retrieves the input tensor(s) of a symbolic operation.Only returns the tensor(s) corresponding to the _first time_the operation was called. |
| output | Retrieves the output tensor(s) of a layer.Only returns the tensor(s) corresponding to the _first time_the operation was called. |
Methods
from_config
@classmethod
from_config( config, custom_objects=None )
Creates a layer from its config.
This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).
| Args | |
|---|---|
| config | A Python dictionary, typically the output of get_config. |
| Returns |
|---|
| A layer instance. |
normalized_weights
normalized_weights()
Generate spectral normalized weights.
This method returns the updated value for self.kernel with the spectral normalized value, so that the layer is ready for call().
symbolic_call
symbolic_call(
*args, **kwargs
)