oneflow.nn.Dropout¶

class oneflow.nn.Dropout(p: float = 0.5, inplace: bool = False, generator=None)¶

During training, randomly zeroes some of the elements of the input tensor with probability 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 \(\frac{1}{1-p}\) during training. This means that during evaluation the module simply computes an identity function.

Additionally, we can pass an extra Tensor addend which shape is consistent with input Tensor. The addend Tensor will be add in result after dropout, it is very useful in model’s residual connection structure.

Parameters

p – probability of an element to be zeroed. Default: 0.5
inplace – If set to True, will do this operation in-place. Default: False
generator – A pseudorandom number generator for sampling

Shape:

Input: \((*)\). Input can be of any shape
Output: \((*)\). Output is of the same shape as input

For example:

example 1:

>>> import numpy as np
>>> import oneflow as flow

>>> m = flow.nn.Dropout(p=0)
>>> arr = np.array(
...    [
...        [-0.7797, 0.2264, 0.2458, 0.4163],
...        [0.4299, 0.3626, -0.4892, 0.4141],
...        [-1.4115, 1.2183, -0.5503, 0.6520],
...    ]
... )
>>> x = flow.Tensor(arr)
>>> y = m(x)
>>> y 
tensor([[-0.7797,  0.2264,  0.2458,  0.4163],
        [ 0.4299,  0.3626, -0.4892,  0.4141],
        [-1.4115,  1.2183, -0.5503,  0.6520]], dtype=oneflow.float32)

example 2:

>>> import numpy as np
>>> import oneflow as flow

>>> m = flow.nn.Dropout(p=0)
>>> arr = np.array(
...    [
...        [-0.7797, 0.2264, 0.2458, 0.4163],
...        [0.4299, 0.3626, -0.4892, 0.4141],
...        [-1.4115, 1.2183, -0.5503, 0.6520],
...    ]
... )
>>> x = flow.Tensor(arr)
>>> addend = flow.ones((3, 4), dtype=flow.float32)
>>> y = m(x, addend=addend)
>>> y 
tensor([[ 0.2203,  1.2264,  1.2458,  1.4163],
        [ 1.4299,  1.3626,  0.5108,  1.4141],
        [-0.4115,  2.2183,  0.4497,  1.6520]], dtype=oneflow.float32)

__init__(p: float = 0.5, inplace: bool = False, generator=None)¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__call__`(args, *kwargs)	Call self as a function.
`__delattr__`(name, /)	Implement delattr(self, name).
`__dir__`()	Default dir() implementation.
`__eq__`(value, /)	Return self==value.
`__format__`(format_spec, /)	Default object formatter.
`__ge__`(value, /)	Return self>=value.
`__getattr__`(name)
`__getattribute__`(name, /)	Return getattr(self, name).
`__gt__`(value, /)	Return self>value.
`__hash__`()	Return hash(self).
`__init__`([p, inplace, generator])	Initialize self.
`__init_subclass__`	This method is called when a class is subclassed.
`__le__`(value, /)	Return self<=value.
`__lt__`(value, /)	Return self<value.
`__ne__`(value, /)	Return self!=value.
`__new__`(**kwargs)	Create and return a new object.
`__reduce__`()	Helper for pickle.
`__reduce_ex__`(protocol, /)	Helper for pickle.
`__repr__`()	Return repr(self).
`__setattr__`(name, value)	Implement setattr(self, name, value).
`__sizeof__`()	Size of object in memory, in bytes.
`__str__`()	Return str(self).
`__subclasshook__`	Abstract classes can override this to customize issubclass().
`_apply`(fn[, applied_dict])
`_get_name`()
`_load_from_state_dict`(state_dict, prefix, …)
`_named_members`(get_members_fn[, prefix, recurse])
`_save_to_state_dict`(destination, prefix, …)
`_shallow_repr`()
`add_module`(name, module)	Adds a child module to the current module.
`apply`(fn)	Applies `fn` recursively to every submodule (as returned by `.children()`) as well as self.
`buffers`([recurse])	Returns an iterator over module buffers.
`children`()	Returns an iterator over immediate children modules.
`cpu`()	Moves all model parameters and buffers to the CPU.
`cuda`([device])	Moves all model parameters and buffers to the GPU.
`double`()	Casts all floating point parameters and buffers to `double` datatype.
`eval`()	Sets the module in evaluation mode.
`extra_repr`()	Set the extra representation of the module
`float`()	Casts all floating point parameters and buffers to `float` datatype.
`forward`(x[, addend])
`half`()	Casts all floating point parameters and buffers to `half` datatype.
`load_state_dict`(state_dict[, strict])	Copies parameters and buffers from `state_dict` into this module and its descendants.
`modules`()	Returns an iterator over all modules in the network.
`named_buffers`([prefix, recurse])	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
`named_children`()	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
`named_modules`([memo, prefix])	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
`named_parameters`([prefix, recurse])	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
`parameters`([recurse])	Returns an iterator over module parameters.
`register_buffer`(name, tensor[, persistent])	Adds a buffer to the module.
`register_forward_hook`(hook)	Registers a forward hook on the module.
`register_forward_pre_hook`(hook)	Registers a forward pre-hook on the module.
`register_parameter`(name, param)	Adds a parameter to the module.
`state_dict`([destination, prefix, keep_vars])	Returns a dictionary containing a whole state of the module.
`to`([device])	Moves the parameters and buffers.
`to_consistent`(args, *kwargs)	This interface is no longer available, please use `oneflow.nn.Module.to_global()` instead.
`to_global`([placement, sbp])	Convert the parameters and buffers to global.
`train`([mode])	Sets the module in training mode.
`zero_grad`([set_to_none])	Sets gradients of all model parameters to zero.

Attributes

`__annotations__`
`__constants__`