class_docstr: Dropout layer
  
  Sets values to zero with probability p. See notes for disabling dropout
  during testing.
  
  Parameters
  ----------
  incoming : a :class:`Layer` instance or a tuple
      the layer feeding into this layer, or the expected input shape
  p : float or scalar tensor
      The probability of setting a value to zero
  rescale : bool
      If ``True`` (the default), scale the input by ``1 / (1 - p)`` when
      dropout is enabled, to keep the expected output mean the same.
  shared_axes : tuple of int
      Axes to share the dropout mask over. By default, each value can be
      dropped individually. ``shared_axes=(0,)`` uses the same mask across
      the batch. ``shared_axes=(2, 3)`` uses the same mask across the
      spatial dimensions of 2D feature maps.
  
  Notes
  -----
  The dropout layer is a regularizer that randomly sets input values to
  zero; see [1]_, [2]_ for why this might improve generalization.
  
  The behaviour of the layer depends on the ``deterministic`` keyword
  argument passed to :func:`lasagne.layers.get_output`. If ``True``, the
  layer behaves deterministically, and passes on the input unchanged. If
  ``False`` or not specified, dropout (and possibly scaling) is enabled.
  Usually, you would use ``deterministic=False`` at train time and
  ``deterministic=True`` at test time.
  
  See also
  --------
  dropout_channels : Drops full channels of feature maps
  spatial_dropout : Alias for :func:`dropout_channels`
  dropout_locations : Drops full pixels or voxels of feature maps
  
  References
  ----------
  .. [1] Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I.,
         Salakhutdinov, R. R. (2012):
         Improving neural networks by preventing co-adaptation of feature
         detectors. arXiv preprint arXiv:1207.0580.
  
  .. [2] Srivastava Nitish, Hinton, G., Krizhevsky, A., Sutskever,
         I., & Salakhutdinov, R. R. (2014):
         Dropout: A Simple Way to Prevent Neural Networks from Overfitting.
         Journal of Machine Learning Research, 5(Jun)(2), 1929-1958.
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code: def __init__(
      self, incoming, p=0.5, rescale=True, shared_axes=(), **kwargs
  ):
      super(DropoutLayer, self).__init__(incoming, **kwargs)
      self._srng = RandomStreams(get_rng().randint(1, 2147462579))
      self.p = p
      self.rescale = rescale
      self.shared_axes = tuple(shared_axes)
  
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 1 to create a new DropoutLayer object from inherited base classes: Layer with example: obj = DropoutLayer(incoming, p, rescale, shared_axes)