class neupy.layers.GaussianNoise[source]

Add gaussian noise to the input value. Mean and standard deviation are layer’s parameters.

std : float

Standard deviation of the gaussian noise. Values needs to be greater than zero. Defaults to 1.

mean : float

Mean of the gaussian noise. Defaults to 0.

name : str or None

Layer’s identifier. If name is equal to None than name will be generated automatically. Defaults to None.

input_shape : tuple

Returns layer’s input shape in the form of a tuple. Shape will not include batch size dimension.

output_shape : tuple

Returns layer’s output shape in the form of a tuple. Shape will not include batch size dimension.

training_state : bool

Defines whether layer in training state or not. Training state will enable some operations inside of the layers that won’t work otherwise.

parameters : dict

Parameters that networks uses during propagation. It might include trainable and non-trainable parameters.

graph : LayerGraph instance

Graphs that stores all relations between layers.


disable_training_state() Context manager that switches off trainig state.
initialize() Set up important configurations related to the layer.
mean = None[source]
options = {'mean': Option(class_name='GaussianNoise', value=NumberProperty(name="mean")), 'name': Option(class_name='BaseLayer', value=Property(name="name")), 'std': Option(class_name='GaussianNoise', value=NumberProperty(name="std"))}[source]

Return output base on the input value.

std = None[source]