tf.nn.weighted_cross_entropy_with_logits(targets=y, logits=y_pred, pos_weight)函数的意思

`tf.nn.weighted_cross_entropy_with_logits`函数是用于计算加权的二分类交叉熵损失的函数。它的输入包括目标值（`targets`）、模型的预测值（`logits`）和正样本的权重（`pos_weight`）。

该函数的作用是计算二分类交叉熵损失，其中正样本的损失会乘以一个权重，可以用来调整正样本的重要性。正样本的权重越大，模型在预测时会更加关注正样本的正确分类。

具体而言，该函数首先将模型的预测值 `logits` 通过Sigmoid函数转换为概率值，然后将概率值与目标值 `targets` 计算交叉熵损失。在计算交叉熵损失时，正样本的损失会乘以 `pos_weight`。

使用示例：

loss = tf.nn.weighted_cross_entropy_with_logits(targets=y, logits=y_pred, pos_weight=pos_weight)

其中，`y` 是目标值（真实标签），`y_pred` 是模型的预测值（未经Sigmoid函数转换的logits），`pos_weight` 是正样本的权重。

需要注意的是，该函数适用于二分类问题，且要求 `logits` 的形状与 `targets` 相同。

相关问题

yolov11损失函数

### YOLOv11 损失函数详解

#### 一、定位误差 (Localization Error)

YOLOv11 中的定位误差用于衡量预测边界框与真实边界框之间的差异。为了更精确地捕捉物体的位置，采用了加权均方根误差(RMSE)，这不仅考虑了中心坐标的偏差还加入了宽高比例的影响[^1]。

def localization_loss(pred_boxes, true_boxes):
    """
    Calculate the weighted RMSE between predicted and ground truth boxes.
    
    Args:
        pred_boxes: Predicted bounding box coordinates from model output.
        true_boxes: Ground-truth bounding box coordinates.

    Returns:
        float: Localization loss value.
    """
    diff = torch.sqrt((pred_boxes[:, :2] - true_boxes[:, :2])**2 + \
                      (torch.sqrt(pred_boxes[:, 2:] * pred_boxes[:, 3:]) - \
                       torch.sqrt(true_boxes[:, 2:] * true_boxes[:, 3:]))**2)
    return torch.mean(diff)

#### 二、置信度误差 (Confidence Error)

置信度误差反映了模型对存在对象与否判断的信心程度。通过交叉熵损失来评估预测置信度与实际标签间的差距，并特别强调正样本的重要性以提高检测精度。

import torch.nn.functional as F

def confidence_loss(pred_confidences, target_confidences):
    """
    Compute binary cross entropy for objectness score prediction
    
    Args:
        pred_confidences: Model's predictions of objects' presence or absence.
        target_confidences: True values indicating whether an object is present.

    Returns:
        float: Confidence error measurement.
    """
    obj_mask = target_confidences == 1
    noobj_mask = ~obj_mask
    
    obj_loss = F.binary_cross_entropy_with_logits(
        pred_confidences[obj_mask], 
        target_confidences[obj_mask])
    noobj_loss = 0.5 * F.binary_cross_entropy_with_logits(
        pred_confidences[noobj_mask],
        target_confidences[noobj_mask])

    return obj_loss + noobj_loss

#### 三、分类误差 (Classification Error)

针对多类别识别任务，分类误差利用softmax后的log损失来进行优化。即使当某个网格内不存在任何目标时也会参与到训练过程中去，从而使得网络能够学习到背景区域的信息特征。

def classification_loss(predictions, targets):
    """
    Applies softmax log loss to multi-class classification task within each grid cell.

    Args:
        predictions: Output logits before applying activation function.
        targets: One-hot encoded class labels per anchor point.

    Returns:
        float: Classification error metric.
    """
    non_empty_cells = targets.sum(dim=-1) != 0
    cls_losses = F.cross_entropy(predictions[non_empty_cells], 
                                targets.argmax(-1)[non_empty_cells],
                                reduction='none')
                                
    return cls_losses.mean()

yolov5中compute loss函数源码

以下是YOLOv5中的compute_loss函数源代码：

def compute_loss(pred, targets, model):
    """
    Compute loss for YOLOv5.
    
    Args:
        pred: (tensor) predictions of the model, shape [batch_size, num_anchors*(num_classes+5), grid_size, grid_size]
        targets: (tensor) targets of the model, shape [batch_size, num_targets, 6], where the last dimension represents
                 [class_id, x, y, w, h, scale], where (x, y) is the center of the object, (w, h) is the width and height
                 of the object, and scale is the scale factor (usually 1).
        model: (nn.Module) YOLOv5 model
        
    Returns:
        loss: (float) total loss
        loss_items: (dict) a dictionary of loss items
    """
    # Constants
    lcls, lbox, lobj = torch.zeros(1, device=pred.device), torch.zeros(1, device=pred.device), torch.zeros(1, device=pred.device)
    tcls, tbox, indices, anchors = build_targets(pred, targets, model)
    num_samples = len(indices)

    # Compute losses
    if num_samples > 0:
        # Classification loss
        lcls = F.binary_cross_entropy_with_logits(pred[..., 5:5 + model.num_classes], tcls, reduction='sum') / num_samples

        # Box regression loss
        lbox = F.mse_loss(pred[..., :4], tbox, reduction='sum') / num_samples

        # Objectness loss
        obj = pred[..., 4:5].sigmoid()
        lobj = F.binary_cross_entropy(obj, indices[..., 0].float(), reduction='sum') / num_samples
        indices = indices[obj > 0.5]
        obj = obj[obj > 0.5]
        boxes = pred[..., :4][obj > 0.5]
        tbox = tbox[obj > 0.5]
        lbox += F.mse_loss(boxes, tbox, reduction='sum') / num_samples

    # Weighted sum of losses
    loss = lobj * model.lambda_obj + lcls * model.lambda_cls + lbox * model.lambda_box
    loss_items = {'loss': loss, 'obj': lobj, 'cls': lcls, 'box': lbox}

    return loss, loss_items

这个函数计算YOLOv5的总损失，包括分类损失，框框回归损失和目标性损失。同时，它还会返回一个字典，其中包含每个损失项的值。