nn.BCEloss加权
时间: 2023-08-27 13:06:02 浏览: 63
nn.BCELoss函数不支持设置权重参数。只有torch.nn.BCEWithLogitsLoss函数支持设置pos_weight参数来处理样本不平衡的问题。pos_weight参数是一个一维张量,大小等于输入张量的大小,用于对正样本进行加权。\[2\]
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- *1* *2* *3* [【pytorch】二元交叉熵损失函数 nn.BCELoss() 与 torch.nn.BCEWithLogitsLoss()](https://blog.csdn.net/weixin_37804469/article/details/129779296)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^koosearch_v1,239^v3^insert_chatgpt"}} ] [.reference_item]
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相关问题
nn.BCELoss推导
nn.BCELoss是二分类交叉熵损失函数,它的推导如下:
假设有n个样本,每个样本的标签为y,预测值为y_hat,则BCELoss的公式为:
BCELoss = -1/n * Σ(y*log(y_hat) + (1-y)*log(1-y_hat))
其中,log表示自然对数。
对公式进行求导,可以得到:
∂BCELoss/∂y_hat = -1/n * (y/y_hat - (1-y)/(1-y_hat))
将其代入梯度下降的公式中,即可进行模型的训练。
需要注意的是,在使用nn.BCELoss时,需要在该层前面加上Sigmoid函数,将输出值映射到[0,1]之间,以便计算损失函数。
torch.nn.BCELoss
BCELoss stands for Binary Cross Entropy Loss. It is a loss function used for binary classification problems where each example belongs to one of two classes. The BCELoss function computes the binary cross-entropy loss between the input and target.
The input to BCELoss is a tensor of predicted probabilities (values between 0 and 1) for each example, and the target is a tensor of binary labels (0 or 1) indicating the true class for each example. The BCELoss function applies the binary cross-entropy formula to compute the loss for each example and then averages the losses over all examples.
The formula for binary cross-entropy loss is:
loss(x, y) = -[y * log(x) + (1 - y) * log(1 - x)]
where x is the predicted probability and y is the binary label.
BCELoss is often used in binary classification problems such as spam detection, fraud detection, and medical diagnosis. It is implemented in PyTorch as torch.nn.BCELoss.
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