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我正在尝试使用 MC Dropout 方法和此链接中提出的解决方案为图像分类任务计算数据集的每个类的熵，以测量 pytorch 上的
不确定性

首先，我计算了每个批次在不同前向传递（class_mean_batch）中每个类的平均值，然后计算了所有测试加载器（classes_mean），然后进行了一些转换以获得（total_mean）以使用它来计算熵，如下面的代码所示

def mcdropout_test(batch_size,n_classes,model,T):

    #set non-dropout layers to eval mode
    model.eval()

    #set dropout layers to train mode
    enable_dropout(model)
    
    softmax = nn.Softmax(dim=1)
    classes_mean = []
       
    for images,labels in testloader:
        images = images.to(device)
        labels = labels.to(device)
        classes_mean_batch = []
            
        with torch.no_grad():
          output_list = []
          
          #getting outputs for T forward passes
          for i in range(T):
            output = model(images)
            output = softmax(output)
            output_list.append(torch.unsqueeze(output, 0))
            
        
        concat_output = torch.cat(output_list,0)
        
        # getting mean of each class per batch across multiple MCD forward passes
        for i in range (n_classes):
          mean = torch.mean(concat_output[:, : , i])
          classes_mean_batch.append(mean)
        
        # getting mean of each class for the testloader
        classes_mean.append(torch.stack(classes_mean_batch))
        

    total_mean = []
    concat_classes_mean = torch.stack(classes_mean)

    for i in range (n_classes):
      concat_classes = concat_classes_mean[: , i]
      total_mean.append(concat_classes)


    total_mean = torch.stack(total_mean)
    total_mean = np.asarray(total_mean.cpu())
 
    epsilon = sys.float_info.min
    # Calculating entropy across multiple MCD forward passes 
    entropy = (- np.sum(total_mean*np.log(total_mean + epsilon), axis=-1)).tolist()
    for i in range(n_classes):
      print(f'The uncertainty of class {i+1} is {entropy[i]:.4f}')

谁能更正或确认我用来计算每个类的熵的实现。

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