How can I calculate the loss (and get a scalar) between two FloatTensors of different size? For example, let’s assume that one FloatTensor is 115x5 and the other is 5x5. How can one get the loss between those two?
loss = (y_predicted - y).pow(2).sum()
but of course it gives an error as the dimensions are different.
You need to explain what your ground-truth values and predictions are.
And if why 115 vs 5 (different batch size or other stuff)?
Because normally it is not logical to do sth like to want. You could resize tensor to same size, but then I think than network will be not learning anything.
I have 10 subjects and each subject has a number of 5-dimensional measurements. So,
Subject_1 with 1, …, N measurements
Subject_2 with 1, …, K measurements
Subject_10 with 1, …, L measurements
For example, I’m trying find the loss between all of the N measurements of subject_1 with my latent sample which is 5 dimensional and I assume it’s drawn from a multivariate Gaussian.