class_docstr: Gaussian noise layer.
  
  Add zero-mean Gaussian noise of given standard deviation to the input [1]_.
  
  Parameters
  ----------
  incoming : a :class:`Layer` instance or a tuple
          the layer feeding into this layer, or the expected input shape
  sigma : float or tensor scalar
          Standard deviation of added Gaussian noise
  
  Notes
  -----
  The Gaussian noise layer is a regularizer. During training you should set
  deterministic to false and during testing you should set deterministic to
  true.
  
  References
  ----------
  .. [1] K.-C. Jim, C. Giles, and B. Horne (1996):
         An analysis of noise in recurrent neural networks: convergence and
         generalization.
         IEEE Transactions on Neural Networks, 7(6):1424-1438.
class_docstr_tok: ["Gaussian","noise","layer",".","Add","zero-mean","Gaussian","noise","of","given","standard","deviation","to","the","input","[","1","]","_.","Parameters","--","--","--","--","--","incoming",":","a",":","class",":","`","Layer","`","instance","or","a","tuple","the","layer","feeding","into","this","layer",",","or","the","expected","input","shape","sigma",":","float","or","tensor","scalar","Standard","deviation","of","added","Gaussian","noise","Notes","--","--","-","The","Gaussian","noise","layer","is","a","regularizer",".","During","training","you","should","set","deterministic","to","false","and","during","testing","you","should","set","deterministic","to","true",".","References","--","--","--","--","--","..","[","1","]","K.-C.","Jim",",","C.","Giles",",","and","B.","Horne","(","1996",")",":","An","analysis","of","noise","in","recurrent","neural","networks",":","convergence","and","generalization",".","IEEE","Transactions","on","Neural","Networks",",","7","(","6",")",":1424-1438","."]
docstr: Parameters
  ----------
  input : tensor
      output from the previous layer
  deterministic : bool
      If true noise is disabled, see notes
docstr_tok: ["Parameters","--","--","--","--","--","input",":","tensor","output","from","the","previous","layer","deterministic",":","bool","If","true","noise","is","disabled",",","see","notes"]
code: def get_output_for(self, input, deterministic=False, **kwargs):
      """
      Parameters
      ----------
      input : tensor
          output from the previous layer
      deterministic : bool
          If true noise is disabled, see notes
      """
      if deterministic or self.sigma == 0:
          return input
      else:
          return input + self._srng.normal(
              input.shape, avg=0.0, std=self.sigma
          )
  
file_path: Lasagne/lasagne/layers/noise.py
imports: import theano
  import theano.tensor
  from .base import Layer
  from ..random import get_rng
  from theano.sandbox.rng_mrg import MRG_RandomStreams
label_desc: Use image node_id 2 for calling the GaussianNoiseLayer obj's underlying member method code with example usage: obj.get_output_for(input, deterministic) and returns: input, unknown