Different results accross multiple runs

Thanks, for your help. I'll check out the link. I already use the same PC and not changing any thing in the algorithm and I can see that loss differs slightly in first epochs of the embeddings network and unfortunately the difference increases. I also use a second classifier that takes these embeddings and get different in accuracy about 4% maximum.

I checked the svm classifier and it gives same results when training data is the same. So the problem is in the feature embeddings network.

I wonder if the ram usage differs across different runs, the results changes?!