Numpy vs Pytorch discrepancy in implementing a loss function?

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I am trying to implement a loss function. But the implementation in numpy is giving me absolute zero where as in torch i am getting 0.33333… . I’ve rechecked the implementation both are same and I am using float64 for both the implementations.

Is this normal or is there something I should be aware of?

Loss function in numpy
Loss function in numpy647×774 26.3 KB

Loss function in torch
Loss function in torch549×810 21.7 KB

I also wonder what impact does it have on my model. Even though predicted == actual the loss is never zero.

Please help me mitigate this.

2

I would claim the error is not “normal” and you might have a bug in your code.
Could you post a minimal and executable code snippet reproducing the error, please?