代码如下: 这个箭头 ) -> Tensor: 什么意思？
def binary_cross_entropy_with_logits(
input: Tensor,
target: Tensor,
weight: Optional[Tensor] = None,
size_average: Optional[bool] = None,
reduce: Optional[bool] = None,
reduction: str = "mean",
pos_weight: Optional[Tensor] = None,
) -> Tensor:
r"""Function that measures Binary Cross Entropy between target and input
logits.
See :class:`~torch.nn.BCEWithLogitsLoss` for details.
Args:
input: Tensor of arbitrary shape as unnormalized scores (often referred to as logits).
target: Tensor of the same shape as input with values between 0 and 1
weight (Tensor, optional): a manual rescaling weight
if provided it's repeated to match input tensor shape
size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
the losses are averaged over each loss element in the batch. Note that for
some losses, there multiple elements per sample. If the field :attr:`size_average`
is set to ``False``, the losses are instead summed for each minibatch. Ignored
when reduce is ``False``. Default: ``True``
reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
losses are averaged or summed over observations for each minibatch depending
on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
batch element instead and ignores :attr:`size_average`. Default: ``True``
reduction (string, optional): Specifies the reduction to apply to the output:
``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
``'mean'``: the sum of the output will be divided by the number of
elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
and :attr:`reduce` are in the process of being deprecated, and in the meantime,
specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``
pos_weight (Tensor, optional): a weight of positive examples.
Must be a vector with length equal to the number of classes.
Examples::
>>> input = torch.randn(3, requires_grad=True)
>>> target = torch.empty(3).random_(2)
>>> loss = F.binary_cross_entropy_with_logits(input, target)
>>> loss.backward()
"""
if has_torch_function_variadic(input, target, weight, pos_weight):
return handle_torch_function(
binary_cross_entropy_with_logits,
(input, target, weight, pos_weight),
input,
target,
weight=weight,
size_average=size_average,
reduce=reduce,
reduction=reduction,
pos_weight=pos_weight,
)
if size_average is not None or reduce is not None:
reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
else:
reduction_enum = _Reduction.get_enum(reduction)
if not (target.size() == input.size()):
raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum)
解题思路：通过肢解分析
1、) 这只是书写规范，正确的完整的书写是def...(),在python中代表tuple元祖数据类型
2、-> 在python中代表返回值注解的符号，作为一种类型注释，给开发、其他人看的，常用于方法中的参数，通过允许将元数据附加到描述其参数和返回值的函数来扩展该功能。
简单示例：
def f(ham:str, eggs:str = 'eggs') -> str:
pass
表示返回值类型应该为str
3、tensor在python中代表张量，是一种数据结构，被广泛用于神经网络模型（编程）中，用来表示或者编码神经网络模型的输入、输出和模型参数。
所以，最终这句话的意思，结合代码前后文意思就是：
返回值的类型张量值应该为如下
input: Tensor,
target: Tensor,
weight: Optional[Tensor] = None,
size_average: Optional[bool] = None,
reduce: Optional[bool] = None,
reduction: str = "mean",
pos_weight: Optional[Tensor] = None,
“->”为函数标注，通常用于类型提示，是python3中引入的用法。
这个箭头符号的意思是告诉你返回的可能是一个什么类型。
例如以下函数接受Tensor、str类型的参数，并返回一个 int 类型的值:
def myfunc(a: Tensor, b: str) -> int :
# 函数体
return 返回值
有帮助的话采纳一下哦！
箭头指这个函数里面的参数都是tensor类型的数据
def clip(text:str, max_len:'int > 0'=80) -> str:
return xxx(str类型)
注意看，函数声明中的各个参数可以在 : 之后增加注解表达式。如果参数有默认值，注解放在参数名和 = 号之间，如上述代码中的max_len:'int > 0'=80。如果想注解返回值，在 ) 和函数声明 末尾的 : 之间添加 -> 和一个表达式。那个表达式可以是任何类型，声明函数返回值的类型
告诉你返回的可能是一个什么类型。
输出结果的类型
->是在python3新增的特性，用来声明一个函数的参数和返回值，在python2.x中缺少注释对函数参数和返回值的标准方法，在开发过程中可以通过各种工具和库来解决该问题，例如可以使用装饰器或者解析函数的docstring来获取。->的目的是提供一个标准方法来注释函数的参数和返回值。更详细信息见PEP 3107 – Function Annotations。