tf.keras.layers.GaussianNoise | TensorFlow v2.16.1 (original) (raw)
tf.keras.layers.GaussianNoise
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Apply additive zero-centered Gaussian noise.
Inherits From: Layer, Operation
tf.keras.layers.GaussianNoise(
stddev, seed=None, **kwargs
)
This is useful to mitigate overfitting (you could see it as a form of random data augmentation). Gaussian Noise (GS) is a natural choice as corruption process for real valued inputs.
As it is a regularization layer, it is only active at training time.
| Args | |
|---|---|
| stddev | Float, standard deviation of the noise distribution. |
| seed | Integer, optional random seed to enable deterministic behavior. |
| Call arguments | |
|---|---|
| inputs | Input tensor (of any rank). |
| training | Python boolean indicating whether the layer should behave in training mode (adding noise) or in inference mode (doing nothing). |
| 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 )
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. |
symbolic_call
symbolic_call(
*args, **kwargs
)