oneflow.nn.CrossEntropyLoss¶
-
class
oneflow.nn.
CrossEntropyLoss
(weight: Optional[oneflow.Tensor] = None, ignore_index: int = - 100, reduction: str = 'mean')¶ This criterion combines
LogSoftmax
andNLLLoss
in one single class.It is useful when training a classification problem with C classes.
The input is expected to contain raw, unnormalized scores for each class.
input has to be a Tensor of size either \((minibatch, C)\) or \((minibatch, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\) for the K-dimensional case (described later).
This criterion expects a class index in the range \([0, C-1]\) as the target for each value of a 1D tensor of size minibatch;
The loss can be described as:
\[\text{loss}(x, class) = -\log\left(\frac{\exp(x[class])}{\sum_j \exp(x[j])}\right) = -x[class] + \log\left(\sum_j \exp(x[j])\right)\]Can also be used for higher dimension inputs, such as 2D images, by providing an input of size \((minibatch, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\), where \(K\) is the number of dimensions, and a target of appropriate shape (see below).
- Parameters
reduction (string, optional) – Specifies the reduction to apply to the output:
'none'
|'mean'
|'sum'
.'none'
: no reduction will be applied,'mean'
: the weighted mean of the output is taken,'sum'
: the output will be summed. Default:'mean'
For example:
>>> import oneflow as flow >>> import numpy as np >>> input = flow.tensor( ... [[-0.1664078, -1.7256707, -0.14690138], ... [-0.21474946, 0.53737473, 0.99684894], ... [-1.135804, -0.50371903, 0.7645404]], dtype=flow.float32) >>> target = flow.tensor(np.array([0, 1, 2]), dtype=flow.int32) >>> out = flow.nn.CrossEntropyLoss(reduction="none")(input, target) >>> out tensor([0.8020, 1.1167, 0.3583], dtype=oneflow.float32) >>> out_sum = flow.nn.CrossEntropyLoss(reduction="sum")(input, target) >>> out_sum tensor(2.2769, dtype=oneflow.float32) >>> out_mean = flow.nn.CrossEntropyLoss(reduction="mean")(input, target) >>> out_mean tensor(0.7590, dtype=oneflow.float32)
-
__init__
(weight: Optional[oneflow.Tensor] = None, ignore_index: int = - 100, reduction: str = 'mean') → 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__
([weight, ignore_index, reduction])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
(input, target)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.