自学教程：基于BCEWithLogitsLoss样本不均衡的处理方案

Examples::     >>> target = torch.ones([10, 64], dtype=torch.float32)  # 64 classes, batch size = 10    >>> output = torch.full([10, 64], 0.999)  # A prediction (logit)    >>> pos_weight = torch.ones([64])  # All weights are equal to 1    >>> criterion = torch.nn.BCEWithLogitsLoss(pos_weight=pos_weight)    >>> criterion(output, target)  # -log(sigmoid(0.999))    tensor(0.3135) Args:    weight (Tensor, optional): a manual rescaling weight given to the loss        of each batch element. If given, has to be a Tensor of size `nbatch`.    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 are 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.

criterion = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([4])) # pos_weight (Tensor, optional): a weight of positive examples.#            Must be a vector with length equal to the number of classes.

Examples::     >>> target = torch.ones([10, 64], dtype=torch.float32)  # 64 classes, batch size = 10    >>> output = torch.full([10, 64], 0.999)  # A prediction (logit)    >>> pos_weight = torch.ones([64])  # All weights are equal to 1    >>> criterion = torch.nn.BCEWithLogitsLoss(pos_weight=pos_weight)    >>> criterion(output, target)  # -log(sigmoid(0.999))    tensor(0.3135)

torch.sigmoid() + torch.nn.BCELoss()

def ce_loss(y_pred, y_train, alpha=1):        p = torch.sigmoid(y_pred)    # p = torch.clamp(p, min=1e-9, max=0.99)      loss = torch.sum(- alpha * torch.log(p) * y_train /           - torch.log(1 - p) * (1 - y_train))/len(y_train)    return loss~

import torchimport torch.nn as nntorch.cuda.manual_seed(300)       # 为当前GPU设置随机种子torch.manual_seed(300)            # 为CPU设置随机种子def ce_loss(y_pred, y_train, alpha=1):   # 计算loss   p = torch.sigmoid(y_pred)   # p = torch.clamp(p, min=1e-9, max=0.99)   loss = torch.sum(- alpha * torch.log(p) * y_train /          - torch.log(1 - p) * (1 - y_train))/len(y_train)   return losspy_lossFun = nn.BCEWithLogitsLoss()input = torch.randn((10000,1), requires_grad=True)target = torch.ones((10000,1))target.requires_grad_(True)py_loss = py_lossFun(input, target)py_loss.backward()print("*********BCEWithLogitsLoss***********")print("loss: ")print(py_loss.item())print("梯度: ")print(input.grad)input = input.detach()input.requires_grad_(True)self_loss = ce_loss(input, target)self_loss.backward()print("*********SelfCELoss***********")print("loss: ")print(self_loss.item())print("梯度: ")print(input.grad)