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python - 来自 sigmoid 函数的模型输出几乎等于 0.5 并且保持不变

问题描述

因此，我一直在针对二值图像分类问题训练 Inception-Resnet V2，在训练时我观察到 logits 不会收敛到 0 或 1。它们只会在 0.5 左右波动。似乎是什么错误？我在一个高度不平衡的数据集上训练了 4 个 epoch 的预训练模型，这就是为什么我也使用加权随机采样器。批量大小为 128，优化器为 adam，学习率为 0.001

device = "cuda"


epochs=4
print("======== Training for ", epochs, "epochs=============")
for epoch in range(epochs):
    total_loss = 0
    model.train()
    print("Training.......")
    print("======== EPOCH #",epoch,"=================")
    tmp_acc = 0
    for i,batch in enumerate(train_loader):
        
        img,label = batch["images"],batch["labels"] 
        
        label = label.type(torch.FloatTensor)
        
        img,label = img.to(device),label.to(device)
        model.zero_grad()
        
        op,aux = model(img)
        
        label_cpu = label.cpu().numpy()
        
        op = F.sigmoid(op)
        
        
        output = op.detach().cpu().numpy()
        tmp_acc += accuracy_score(output,label_cpu)
        loss = criterion(op,label)
        total_loss = loss.item()
        loss.backward()
        
        adam.step()
        if(i%10==0 and i>0):
            print("STEP: ",i, "of steps ",len(train_loader))
        
            print("Current loss: ",total_loss/i)
            print("Training Accuracy ",tmp_acc/i)
            print("OP",op)
            print("Label",label_cpu)
        
        
        
    avg_loss = total_loss/len(train_loader)
    print("The loss after ",epoch," epochs is ",avg_loss)
    
    model.eval()
    print("Validating.....")
    tmp_accuracy = 0
    z_count,o_count=0,0
    z_count_truth,o_count_truth = 0,0
    
    for i,batch in enumerate(val_loader):
        img,label = batch["images"],batch["labels"]
        img = img.to(device)
        with torch.no_grad():
            op = F.sigmoid(model(img))
        op_cpu = op.detach().cpu().numpy()
        label = label.numpy()
        tmp_accuracy += accuracy_score(op_cpu,label)
        z_count += np.sum(op_cpu==0)
        o_count += np.sum(op_cpu==1)
        z_count_truth += np.sum(label==0)
        o_count_truth += np.sum(label==1)
    percent_correct_z = z_count/z_count_truth
    percent_correct_o = o_count/o_count_truth
    accuracy = tmp_accuracy/len(val_loader)
    print("Accuracy: ", "is ",accuracy)
    #print("Percent of correct zero labels ",percent_correct_z)
    #print("Percent of correct one labels ",percent_correct_o)```

输出看起来像

STEP:  90 of steps  99
Current loss:  0.007694996065563626
Training Accuracy  0.5019965277777778
OP tensor([[0.4962],
        [0.4956],
        [0.4950],
        [0.4957],
        [0.4945],
        [0.4957],
        [0.4952],
        [0.4965],
        [0.4950],
        [0.4962],
        [0.4956],
        [0.4956],
        [0.4951],
        [0.4953],
        [0.4956],
        [0.4958],
        [0.4949],
        [0.4945],
        [0.4955],
        [0.4924],
        [0.4952],
        [0.4952],
        [0.4958],
        [0.4953],
        [0.4959],
        [0.4952],
        [0.4965],
        [0.4956],
        [0.4956],
        [0.4381],
        [0.4951],
        [0.4946],
        [0.4957],
        [0.4951],
        [0.4955],
        [0.4952],
        [0.4955],
        [0.4948],
        [0.4951],
        [0.4960],
        [0.4956],
        [0.4955],
        [0.4958],
        [0.4957],
        [0.4953],
        [0.4954],
        [0.4955],
        [0.4959],
        [0.4949],
        [0.4960],
        [0.4953],
        [0.4949],
        [0.4951],
        [0.4952],
        [0.4949],
        [0.4954],
        [0.4956],
        [0.4951],
        [0.4947],
        [0.4958],
        [0.4953],
        [0.4960],
        [0.4959],
        [0.4958],
        [0.4948],
        [0.4947],
        [0.4957],
        [0.4961],
        [0.4955],
        [0.4959],
        [0.4955],
        [0.4954],
        [0.4959],
        [0.4952],
        [0.4955],
        [0.4951],
        [0.4962],
        [0.4961],
        [0.4961],
        [0.4960],
        [0.4956],
        [0.4959],
        [0.4953],
        [0.4960],
        [0.4955],
        [0.4949],
        [0.4958],
        [0.4953],
        [0.4955],
        [0.4959],
        [0.4951],
        [0.4961],
        [0.4939],
        [0.4954],
        [0.4953],
        [0.4958],
        [0.4953],
        [0.4949],
        [0.4959],
        [0.4958],
        [0.4960],
        [0.4949],
        [0.4957],
        [0.4964],
        [0.4949],
        [0.4956],
        [0.4952],
        [0.4959],
        [0.4954],
        [0.4958],
        [0.4954],
        [0.4951],
        [0.4953],
        [0.4953],
        [0.4958],
        [0.4954],
        [0.4955],
        [0.4954],
        [0.4960],
        [0.4946],
        [0.4950],
        [0.4953],
        [0.4957],
        [0.4956],
        [0.4954],
        [0.4940],
        [0.4951],
        [0.4955]], device='cuda:0', grad_fn=<SigmoidBackward>)
Label [0. 1. 0. 1. 1. 0. 1. 0. 1. 1. 1. 0. 0. 0. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0.
 1. 0. 0. 1. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 1. 0. 1. 1.
 0. 0. 1. 1. 1. 0. 1. 0. 0. 0. 1. 1. 1. 1. 0. 1. 0. 1. 1. 0. 0. 0. 1. 1.
 0. 1. 1. 0. 0. 0. 1. 1. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 1. 1. 0.
 0. 1. 1. 0. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.
 1. 0. 1. 1. 0. 1. 1. 0.]

OP Tensor 对应输出 logits 标签对应原始标签

标签： pythondeep-learningcomputer-visionpytorchartificial-intelligence

解决方案

既然你有

op = F.sigmoid(op)

并且所有的输出值都在 左右0.5，从 sigmoid 函数的定义看来，它的所有输入值都非常接近0。这意味着很可能您的所有输入图像都为零，或者网络的权重没有正确初始化。由于 resnet v2 有很多跳过连接，因此问题似乎更有可能来自您的输入图像。

作为一般的初始测试，查看您的网络是否能够过度拟合非常小的数据集通常很有用。在您的情况下，我会首先尝试将单个图像过度拟合到其标签上，这将使调试比您当前的批量大小和改组更容易。

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