torch/nn/modules/dropout.py - platform/external/pytorch - Git at Google

 from .module import Module
 from .. import functional as F

 from torch import Tensor

 __all__ = ['Dropout', 'Dropout1d', 'Dropout2d', 'Dropout3d', 'AlphaDropout', 'FeatureAlphaDropout']

 class _DropoutNd(Module):
     __constants__ = ['p', 'inplace']
     p: float
     inplace: bool

     def __init__(self, p: float = 0.5, inplace: bool = False) -> None:
         super().__init__()
         if p < 0 or p > 1:
             raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
         self.p = p
         self.inplace = inplace

     def extra_repr(self) -> str:
         return f'p={self.p}, inplace={self.inplace}'


 class Dropout(_DropoutNd):
     r"""During training, randomly zeroes some of the elements of the input
     tensor with probability :attr:`p` using samples from a Bernoulli
     distribution. Each channel will be zeroed out independently on every forward
     call.

     This has proven to be an effective technique for regularization and
     preventing the co-adaptation of neurons as described in the paper
     `Improving neural networks by preventing co-adaptation of feature
     detectors`_ .

     Furthermore, the outputs are scaled by a factor of :math:`\frac{1}{1-p}` during
     training. This means that during evaluation the module simply computes an
     identity function.

     Args:
         p: probability of an element to be zeroed. Default: 0.5
         inplace: If set to ``True``, will do this operation in-place. Default: ``False``

     Shape:
         - Input: :math:`(*)`. Input can be of any shape
         - Output: :math:`(*)`. Output is of the same shape as input

     Examples::

         >>> m = nn.Dropout(p=0.2)
         >>> input = torch.randn(20, 16)
         >>> output = m(input)

     .. _Improving neural networks by preventing co-adaptation of feature
         detectors: https://arxiv.org/abs/1207.0580
     """

     def forward(self, input: Tensor) -> Tensor:
         return F.dropout(input, self.p, self.training, self.inplace)


 class Dropout1d(_DropoutNd):
     r"""Randomly zero out entire channels (a channel is a 1D feature map,
     e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
     batched input is a 1D tensor :math:`\text{input}[i, j]`).
     Each channel will be zeroed out independently on every forward call with
     probability :attr:`p` using samples from a Bernoulli distribution.

     Usually the input comes from :class:`nn.Conv1d` modules.

     As described in the paper
     `Efficient Object Localization Using Convolutional Networks`_ ,
     if adjacent pixels within feature maps are strongly correlated
     (as is normally the case in early convolution layers) then i.i.d. dropout
     will not regularize the activations and will otherwise just result
     in an effective learning rate decrease.

     In this case, :func:`nn.Dropout1d` will help promote independence between
     feature maps and should be used instead.

     Args:
         p (float, optional): probability of an element to be zero-ed.
         inplace (bool, optional): If set to ``True``, will do this operation
             in-place

     Shape:
         - Input: :math:`(N, C, L)` or :math:`(C, L)`.
         - Output: :math:`(N, C, L)` or :math:`(C, L)` (same shape as input).

     Examples::

         >>> m = nn.Dropout1d(p=0.2)
         >>> input = torch.randn(20, 16, 32)
         >>> output = m(input)

     .. _Efficient Object Localization Using Convolutional Networks:
        https://arxiv.org/abs/1411.4280
     """

     def forward(self, input: Tensor) -> Tensor:
         return F.dropout1d(input, self.p, self.training, self.inplace)


 class Dropout2d(_DropoutNd):
     r"""Randomly zero out entire channels (a channel is a 2D feature map,
     e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
     batched input is a 2D tensor :math:`\text{input}[i, j]`).
     Each channel will be zeroed out independently on every forward call with
     probability :attr:`p` using samples from a Bernoulli distribution.

     Usually the input comes from :class:`nn.Conv2d` modules.

     As described in the paper
     `Efficient Object Localization Using Convolutional Networks`_ ,
     if adjacent pixels within feature maps are strongly correlated
     (as is normally the case in early convolution layers) then i.i.d. dropout
     will not regularize the activations and will otherwise just result
     in an effective learning rate decrease.

     In this case, :func:`nn.Dropout2d` will help promote independence between
     feature maps and should be used instead.

     Args:
         p (float, optional): probability of an element to be zero-ed.
         inplace (bool, optional): If set to ``True``, will do this operation
             in-place

     .. warning ::
         Due to historical reasons, this class will perform 1D channel-wise dropout
         for 3D inputs (as done by :class:`nn.Dropout1d`). Thus, it currently does NOT
         support inputs without a batch dimension of shape :math:`(C, H, W)`. This
         behavior will change in a future release to interpret 3D inputs as no-batch-dim
         inputs. To maintain the old behavior, switch to :class:`nn.Dropout1d`.

     Shape:
         - Input: :math:`(N, C, H, W)` or :math:`(N, C, L)`.
         - Output: :math:`(N, C, H, W)` or :math:`(N, C, L)` (same shape as input).

     Examples::

         >>> m = nn.Dropout2d(p=0.2)
         >>> input = torch.randn(20, 16, 32, 32)
         >>> output = m(input)

     .. _Efficient Object Localization Using Convolutional Networks:
        https://arxiv.org/abs/1411.4280
     """

     def forward(self, input: Tensor) -> Tensor:
         return F.dropout2d(input, self.p, self.training, self.inplace)


 class Dropout3d(_DropoutNd):
     r"""Randomly zero out entire channels (a channel is a 3D feature map,
     e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
     batched input is a 3D tensor :math:`\text{input}[i, j]`).
     Each channel will be zeroed out independently on every forward call with
     probability :attr:`p` using samples from a Bernoulli distribution.

     Usually the input comes from :class:`nn.Conv3d` modules.

     As described in the paper
     `Efficient Object Localization Using Convolutional Networks`_ ,
     if adjacent pixels within feature maps are strongly correlated
     (as is normally the case in early convolution layers) then i.i.d. dropout
     will not regularize the activations and will otherwise just result
     in an effective learning rate decrease.

     In this case, :func:`nn.Dropout3d` will help promote independence between
     feature maps and should be used instead.

     Args:
         p (float, optional): probability of an element to be zeroed.
         inplace (bool, optional): If set to ``True``, will do this operation
             in-place

     Shape:
         - Input: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)`.
         - Output: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)` (same shape as input).

     Examples::

         >>> m = nn.Dropout3d(p=0.2)
         >>> input = torch.randn(20, 16, 4, 32, 32)
         >>> output = m(input)

     .. _Efficient Object Localization Using Convolutional Networks:
        https://arxiv.org/abs/1411.4280
     """

     def forward(self, input: Tensor) -> Tensor:
         return F.dropout3d(input, self.p, self.training, self.inplace)


 class AlphaDropout(_DropoutNd):
     r"""Applies Alpha Dropout over the input.

     Alpha Dropout is a type of Dropout that maintains the self-normalizing
     property.
     For an input with zero mean and unit standard deviation, the output of
     Alpha Dropout maintains the original mean and standard deviation of the
     input.
     Alpha Dropout goes hand-in-hand with SELU activation function, which ensures
     that the outputs have zero mean and unit standard deviation.

     During training, it randomly masks some of the elements of the input
     tensor with probability *p* using samples from a bernoulli distribution.
     The elements to masked are randomized on every forward call, and scaled
     and shifted to maintain zero mean and unit standard deviation.

     During evaluation the module simply computes an identity function.

     More details can be found in the paper `Self-Normalizing Neural Networks`_ .

     Args:
         p (float): probability of an element to be dropped. Default: 0.5
         inplace (bool, optional): If set to ``True``, will do this operation
             in-place

     Shape:
         - Input: :math:`(*)`. Input can be of any shape
         - Output: :math:`(*)`. Output is of the same shape as input

     Examples::

         >>> m = nn.AlphaDropout(p=0.2)
         >>> input = torch.randn(20, 16)
         >>> output = m(input)

     .. _Self-Normalizing Neural Networks: https://arxiv.org/abs/1706.02515
     """

     def forward(self, input: Tensor) -> Tensor:
         return F.alpha_dropout(input, self.p, self.training)


 class FeatureAlphaDropout(_DropoutNd):
     r"""Randomly masks out entire channels (a channel is a feature map,
     e.g. the :math:`j`-th channel of the :math:`i`-th sample in the batch input
     is a tensor :math:`\text{input}[i, j]`) of the input tensor). Instead of
     setting activations to zero, as in regular Dropout, the activations are set
     to the negative saturation value of the SELU activation function. More details
     can be found in the paper `Self-Normalizing Neural Networks`_ .

     Each element will be masked independently for each sample on every forward
     call with probability :attr:`p` using samples from a Bernoulli distribution.
     The elements to be masked are randomized on every forward call, and scaled
     and shifted to maintain zero mean and unit variance.

     Usually the input comes from :class:`nn.AlphaDropout` modules.

     As described in the paper
     `Efficient Object Localization Using Convolutional Networks`_ ,
     if adjacent pixels within feature maps are strongly correlated
     (as is normally the case in early convolution layers) then i.i.d. dropout
     will not regularize the activations and will otherwise just result
     in an effective learning rate decrease.

     In this case, :func:`nn.AlphaDropout` will help promote independence between
     feature maps and should be used instead.

     Args:
         p (float, optional): probability of an element to be zeroed. Default: 0.5
         inplace (bool, optional): If set to ``True``, will do this operation
             in-place

     Shape:
         - Input: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)`.
         - Output: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)` (same shape as input).

     Examples::

         >>> m = nn.FeatureAlphaDropout(p=0.2)
         >>> input = torch.randn(20, 16, 4, 32, 32)
         >>> output = m(input)

     .. _Self-Normalizing Neural Networks: https://arxiv.org/abs/1706.02515
     .. _Efficient Object Localization Using Convolutional Networks:
        https://arxiv.org/abs/1411.4280
     """

     def forward(self, input: Tensor) -> Tensor:
         return F.feature_alpha_dropout(input, self.p, self.training)
	from .module import Module
	from .. import functional as F

	from torch import Tensor

	__all__ = ['Dropout', 'Dropout1d', 'Dropout2d', 'Dropout3d', 'AlphaDropout', 'FeatureAlphaDropout']

	class _DropoutNd(Module):
	__constants__ = ['p', 'inplace']
	p: float
	inplace: bool

	def __init__(self, p: float = 0.5, inplace: bool = False) -> None:
	super().__init__()
	if p < 0 or p > 1:
	raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
	self.p = p
	self.inplace = inplace

	def extra_repr(self) -> str:
	return f'p={self.p}, inplace={self.inplace}'


	class Dropout(_DropoutNd):
	r"""During training, randomly zeroes some of the elements of the input
	tensor with probability :attr:`p` using samples from a Bernoulli
	distribution. Each channel will be zeroed out independently on every forward
	call.

	This has proven to be an effective technique for regularization and
	preventing the co-adaptation of neurons as described in the paper
	`Improving neural networks by preventing co-adaptation of feature
	detectors`_ .

	Furthermore, the outputs are scaled by a factor of :math:`\frac{1}{1-p}` during
	training. This means that during evaluation the module simply computes an
	identity function.

	Args:
	p: probability of an element to be zeroed. Default: 0.5
	inplace: If set to ``True``, will do this operation in-place. Default: ``False``

	Shape:
	- Input: :math:`(*)`. Input can be of any shape
	- Output: :math:`(*)`. Output is of the same shape as input

	Examples::

	>>> m = nn.Dropout(p=0.2)
	>>> input = torch.randn(20, 16)
	>>> output = m(input)

	.. _Improving neural networks by preventing co-adaptation of feature
	detectors: https://arxiv.org/abs/1207.0580
	"""

	def forward(self, input: Tensor) -> Tensor:
	return F.dropout(input, self.p, self.training, self.inplace)


	class Dropout1d(_DropoutNd):
	r"""Randomly zero out entire channels (a channel is a 1D feature map,
	e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
	batched input is a 1D tensor :math:`\text{input}[i, j]`).
	Each channel will be zeroed out independently on every forward call with
	probability :attr:`p` using samples from a Bernoulli distribution.

	Usually the input comes from :class:`nn.Conv1d` modules.

	As described in the paper
	`Efficient Object Localization Using Convolutional Networks`_ ,
	if adjacent pixels within feature maps are strongly correlated
	(as is normally the case in early convolution layers) then i.i.d. dropout
	will not regularize the activations and will otherwise just result
	in an effective learning rate decrease.

	In this case, :func:`nn.Dropout1d` will help promote independence between
	feature maps and should be used instead.

	Args:
	p (float, optional): probability of an element to be zero-ed.
	inplace (bool, optional): If set to ``True``, will do this operation
	in-place

	Shape:
	- Input: :math:`(N, C, L)` or :math:`(C, L)`.
	- Output: :math:`(N, C, L)` or :math:`(C, L)` (same shape as input).

	Examples::

	>>> m = nn.Dropout1d(p=0.2)
	>>> input = torch.randn(20, 16, 32)
	>>> output = m(input)

	.. _Efficient Object Localization Using Convolutional Networks:
	https://arxiv.org/abs/1411.4280
	"""

	def forward(self, input: Tensor) -> Tensor:
	return F.dropout1d(input, self.p, self.training, self.inplace)


	class Dropout2d(_DropoutNd):
	r"""Randomly zero out entire channels (a channel is a 2D feature map,
	e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
	batched input is a 2D tensor :math:`\text{input}[i, j]`).
	Each channel will be zeroed out independently on every forward call with
	probability :attr:`p` using samples from a Bernoulli distribution.

	Usually the input comes from :class:`nn.Conv2d` modules.

	As described in the paper
	`Efficient Object Localization Using Convolutional Networks`_ ,
	if adjacent pixels within feature maps are strongly correlated
	(as is normally the case in early convolution layers) then i.i.d. dropout
	will not regularize the activations and will otherwise just result
	in an effective learning rate decrease.

	In this case, :func:`nn.Dropout2d` will help promote independence between
	feature maps and should be used instead.

	Args:
	p (float, optional): probability of an element to be zero-ed.
	inplace (bool, optional): If set to ``True``, will do this operation
	in-place

	.. warning ::
	Due to historical reasons, this class will perform 1D channel-wise dropout
	for 3D inputs (as done by :class:`nn.Dropout1d`). Thus, it currently does NOT
	support inputs without a batch dimension of shape :math:`(C, H, W)`. This
	behavior will change in a future release to interpret 3D inputs as no-batch-dim
	inputs. To maintain the old behavior, switch to :class:`nn.Dropout1d`.

	Shape:
	- Input: :math:`(N, C, H, W)` or :math:`(N, C, L)`.
	- Output: :math:`(N, C, H, W)` or :math:`(N, C, L)` (same shape as input).

	Examples::

	>>> m = nn.Dropout2d(p=0.2)
	>>> input = torch.randn(20, 16, 32, 32)
	>>> output = m(input)

	.. _Efficient Object Localization Using Convolutional Networks:
	https://arxiv.org/abs/1411.4280
	"""

	def forward(self, input: Tensor) -> Tensor:
	return F.dropout2d(input, self.p, self.training, self.inplace)


	class Dropout3d(_DropoutNd):
	r"""Randomly zero out entire channels (a channel is a 3D feature map,
	e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
	batched input is a 3D tensor :math:`\text{input}[i, j]`).
	Each channel will be zeroed out independently on every forward call with
	probability :attr:`p` using samples from a Bernoulli distribution.

	Usually the input comes from :class:`nn.Conv3d` modules.

	As described in the paper
	`Efficient Object Localization Using Convolutional Networks`_ ,
	if adjacent pixels within feature maps are strongly correlated
	(as is normally the case in early convolution layers) then i.i.d. dropout
	will not regularize the activations and will otherwise just result
	in an effective learning rate decrease.

	In this case, :func:`nn.Dropout3d` will help promote independence between
	feature maps and should be used instead.

	Args:
	p (float, optional): probability of an element to be zeroed.
	inplace (bool, optional): If set to ``True``, will do this operation
	in-place

	Shape:
	- Input: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)`.
	- Output: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)` (same shape as input).

	Examples::

	>>> m = nn.Dropout3d(p=0.2)
	>>> input = torch.randn(20, 16, 4, 32, 32)
	>>> output = m(input)

	.. _Efficient Object Localization Using Convolutional Networks:
	https://arxiv.org/abs/1411.4280
	"""

	def forward(self, input: Tensor) -> Tensor:
	return F.dropout3d(input, self.p, self.training, self.inplace)


	class AlphaDropout(_DropoutNd):
	r"""Applies Alpha Dropout over the input.

	Alpha Dropout is a type of Dropout that maintains the self-normalizing
	property.
	For an input with zero mean and unit standard deviation, the output of
	Alpha Dropout maintains the original mean and standard deviation of the
	input.
	Alpha Dropout goes hand-in-hand with SELU activation function, which ensures
	that the outputs have zero mean and unit standard deviation.

	During training, it randomly masks some of the elements of the input
	tensor with probability p using samples from a bernoulli distribution.
	The elements to masked are randomized on every forward call, and scaled
	and shifted to maintain zero mean and unit standard deviation.

	During evaluation the module simply computes an identity function.

	More details can be found in the paper `Self-Normalizing Neural Networks`_ .

	Args:
	p (float): probability of an element to be dropped. Default: 0.5
	inplace (bool, optional): If set to ``True``, will do this operation
	in-place

	Shape:
	- Input: :math:`(*)`. Input can be of any shape
	- Output: :math:`(*)`. Output is of the same shape as input

	Examples::

	>>> m = nn.AlphaDropout(p=0.2)
	>>> input = torch.randn(20, 16)
	>>> output = m(input)

	.. _Self-Normalizing Neural Networks: https://arxiv.org/abs/1706.02515
	"""

	def forward(self, input: Tensor) -> Tensor:
	return F.alpha_dropout(input, self.p, self.training)


	class FeatureAlphaDropout(_DropoutNd):
	r"""Randomly masks out entire channels (a channel is a feature map,
	e.g. the :math:`j`-th channel of the :math:`i`-th sample in the batch input
	is a tensor :math:`\text{input}[i, j]`) of the input tensor). Instead of
	setting activations to zero, as in regular Dropout, the activations are set
	to the negative saturation value of the SELU activation function. More details
	can be found in the paper `Self-Normalizing Neural Networks`_ .

	Each element will be masked independently for each sample on every forward
	call with probability :attr:`p` using samples from a Bernoulli distribution.
	The elements to be masked are randomized on every forward call, and scaled
	and shifted to maintain zero mean and unit variance.

	Usually the input comes from :class:`nn.AlphaDropout` modules.

	As described in the paper
	`Efficient Object Localization Using Convolutional Networks`_ ,
	if adjacent pixels within feature maps are strongly correlated
	(as is normally the case in early convolution layers) then i.i.d. dropout
	will not regularize the activations and will otherwise just result
	in an effective learning rate decrease.

	In this case, :func:`nn.AlphaDropout` will help promote independence between
	feature maps and should be used instead.

	Args:
	p (float, optional): probability of an element to be zeroed. Default: 0.5
	inplace (bool, optional): If set to ``True``, will do this operation
	in-place

	Shape:
	- Input: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)`.
	- Output: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)` (same shape as input).

	Examples::

	>>> m = nn.FeatureAlphaDropout(p=0.2)
	>>> input = torch.randn(20, 16, 4, 32, 32)
	>>> output = m(input)

	.. _Self-Normalizing Neural Networks: https://arxiv.org/abs/1706.02515
	.. _Efficient Object Localization Using Convolutional Networks:
	https://arxiv.org/abs/1411.4280
	"""

	def forward(self, input: Tensor) -> Tensor:
	return F.feature_alpha_dropout(input, self.p, self.training)