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CLIP Blog Last Code Bit
Hey Aman,
Great blog and reads really well. I had a question regarding the last part of the pseudo-code provided,
labels = np.arange(n) # will be something like (0, 1, 2, 3, ...., n - 1) shape (n, )
loss_i = cross_entropy_loss(logits, labels, axis=0) # logits are shaped (n, n)
loss_t = cross_entropy_loss(logits, labels, axis=1)
loss = (loss_i + loss_t)/2
Can you maybe explain how this enables the cosine similarity to be high along the diagonal and low along off-diagonal elements? I added some comments on the side for my own understanding.
Also, a suggestion would be to enable comments, so that people having similar doubts can look at the comments.
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