When I come back to this, it is still a classification problem. My output is with says [-0.358118 -0.0280287 -0.30103 -0.129338]. They are not discrete like example above. They are continuous real values. Not possible for doing that. I have also looked into the mentioned transfer learning, again it is a classification example. Any suggestions. Thanks.

Hi @SuFong_Chien, we’re sorry for not getting back to you sooner! I missed this comment.

If I understand correctly, you would like to predict an output label for one of four classes? Given the vector `[-0.358118 -0.0280287 -0.30103 -0.129338]`

, this could be achieved by applying a softmax (e.g., using `tf.nn.softmax`

) and finding a resulting probability vector. To find the prediction, you could then do `tf.math.argmax`

or alternatively sample from the vector.

However, one thing that is not clear to me is that the values you have are negative. If I recall correctly, these values should be associated with probabilities of getting certain numbers of photons in each mode, so we expect them to be non-negative and sum to less than one. If that were the case, instead of a softmax, we could also just renormalize.

Hope this helps answer your question, and let us know if you have any more!

Thanks