Questions about the loss function

[handsomelys]

I'm interested in how to implement InfoNCE in your code using torch.nn.CrossEntropyLoss() function, but I don't seem to have found good learning material, can you explain to me why InfoNCE can be implemented in this way in the provided code?

In addition, I would like to ask, how should this part of the code be understood?

# head + relation -> tail
loss = self. Criterion(logits, labels)
# tail -> head + relation
loss += self. Criterion(logits[:, :batch_size].t(), labels)

How to understand "tail -> head + relation"？
I would appreciate your help! Looking forward to your reply！

[intfloat]

You can refer to the answer here: #10 (comment)

The InfoNCE loss is basically a cross entropy loss but the labels are not pre-defined like in text classification.

[handsomelys]

thanks for your reply!

[cyjie429]

You can refer to the answer here: #10 (comment)

The InfoNCE loss is basically a cross entropy loss but the labels are not pre-defined like in text classification.

您好，请问是否可以仅使用head + relation -> tail？

[intfloat]

当然可以，把第二行loss += ...注释掉就行，但效果会下降一点

[cyjie429]

非常感谢

[cyjie429]

当然可以，把第二行loss += ...注释掉就行，但效果会下降一点

请问我在训练过程中将第二行 Loss+=...注释之后，在测试过程中也需要注释backward_metrics = .....吗

[cyjie429]

当然可以，把第二行loss += ...注释掉就行，但效果会下降一点

因为我每次看最终结果 好像forward metrics的效果会更好

[intfloat]

当然可以，把第二行loss += ...注释掉就行，但效果会下降一点

请问我在训练过程中将第二行 Loss+=...注释之后，在测试过程中也需要注释backward_metrics = .....吗

不需要，forward metrics 对应 给定头实体和关系，预测尾实体，backward metrics 对应 给定尾实体和关系，预测头实体。对大多数数据集来说，第一种预测任务更简单，所以metrics会好一些。

[cyjie429]

当然可以，把第二行loss += ...注释掉就行，但效果会下降一点

请问我在训练过程中将第二行 Loss+=...注释之后，在测试过程中也需要注释backward_metrics = .....吗

不需要，forward metrics 对应 给定头实体和关系，预测尾实体，backward metrics 对应 给定尾实体和关系，预测头实体。对大多数数据集来说，第一种预测任务更简单，所以metrics会好一些。

懂了 谢谢