Hello,

I’m trying to do regression with an LSTM and mini batching.

So I have 7 features of different ranges, therefore I’m scaling them between 0 and 1.

My first attempt was to not scale the target variable, which ranges from 500-1000, but the model couldn’t learn anything. So after reading some posts, that’s because the initial output of my model is too much away from the output, and therefore I will get huge losses.

So if I also scale the target variable the model seems to learn quite good.

Inside a epoch I’m calculating the mse of the model after has seen a batch. At the end of a epoch I sum up the losses per batch and divide it by the number of batches, right?

How can I interpret this loss value now? It’s really small, about 0.01. But isn’t that only because I scaled my target between 0 and 1? How can I get from this value to the “real” mse between the original target values, because this mse would be quite bigger, right?

Is 0.01 really shows how well the model performance on my actual original target values??

Thanks for helping