How to add cosine similarity score in Cross Entropy Loss

nlp
2

Since you would like to maximize the cosine similarity, I would go with the first approach, as in the worst case, you’ll add 0.01 * 2 to the loss and in the best (trained) case, it will be 1 - 1 = 0. Here is a small dummy example of just rotating tensors:

x = torch.tensor([[1., 1.]], requires_grad=True)
y = torch.tensor([[-1, 0.]])

criterion = nn.CosineSimilarity()
optimizer = torch.optim.SGD([x], lr=1.)

for _ in range(20):
    optimizer.zero_grad()
    loss = 1. - criterion(x, y)
    loss.backward()
    optimizer.step()
    print('x: {}, loss: {}'.format(x, loss.item()))
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