class neupy.layers.GaussianNoise[source]

Add gaussian noise to the input value. Mean and standard deviation of the noise can be controlled from the layers parameters.

It’s important to note that output from the layer is controled by the training parameter in the output method. Layer will be applied only in cases when training=True propagated through the network, otherwise it will act as an identity.

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 name. Can be used as a reference to specific layer. Name Can be specified as:

  • String: Specified name will be used as a direct reference to the layer. For example, name=”fc”
  • Format string: Name pattern could be defined as a format string and specified field will be replaced with an index. For example, name=”fc{}” will be replaced with fc1, fc2 and so on. A bit more complex formatting methods are acceptable, for example, name=”fc-{:<03d}” will be converted to fc-001, fc-002, fc-003 and so on.
  • None: When value specified as None than name will be generated from the class name.

Defaults to None.


>>> from neupy.layers import *
>>> network = join(
...     Input(10),
...     Relu(5) >> GaussianNoise(std=0.1),
...     Relu(5) >> GaussianNoise(std=0.1),
...     Sigmoid(1),
... )
>>> network
(?, 10) -> [... 6 layers ...] -> (?, 1)
variables : dict

Variable names and their values. Dictionary can be empty in case if variables hasn’t been created yet.


variable(value, name, shape=None, trainable=True) Initializes variable with specified values.
get_output_shape(input_shape) Computes expected output shape from the layer based on the specified input shape.
output(*inputs, **kwargs) Propagates input through the layer. The kwargs variable might contain additional information that propagates through the network.
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]
output(input_value, training=False)[source]
std = None[source]