如何使用pytorch从Resnet获取概率？

问题描述

我正在微调具有多个标签的数据集上的resnet。

I am finetuning resnet on my dataset which has multiple labels.

我想将分类层的得分转换为概率，并使用这些概率来计算训练时的损失。

I would like to convert the 'scores' of the classification layer to probabilities and use those probabilities to calculate the loss at the training.

您能为此提供示例代码吗？
我可以这样使用吗：

Could you give an example code for this?Can I use like this:

       P = net.forward(x)
       p = torch.nn.functional.softmax(P, dim=1)
       loss = torch.nn.functional.cross_entropy(P, y)

我不清楚这是否正确，因为我正在传递概率作为交叉熵损失的输入。

I am unclear whether this is the correct way or not as I am passing probabilities as the input to crossentropy loss.

推荐答案

因此，您正在训练模型，即在pytorch中具有交叉熵的resnet。您的损失计算将如下所示。

So, you are training a model i.e resnet with cross-entropy in pytorch. Your loss calculation would look like this.

logit = model(x)
loss = torch.nn.functional.cross_entropy(logits=logit, target=y)

在这种情况下，您可以计算所有概率

In this case, you can calculate the probabilities of all classes by doing,

logit = model(x)
p = torch.nn.functional.softmax(logit, dim=1)
# to calculate loss using probabilities you can do below 
loss = torch.nn.functional.nll_loss(torch.log(p), y)

请注意，如果您使用概率，则必须手动记录 log ，这很糟糕由于数字原因。而是使用 log_softmax 或 cross_entropy ，在这种情况下，您可能最终会使用交叉熵和概率计算来计算损失。

Note that if you use probabilities you will have to manually take a log, which is bad for numerical reasons. Instead, either use log_softmax or cross_entropy in which case you may end up computing losses using cross entropy and computing probability separately.

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