Hi everyone, probably this is a silly question but I will appreciate if someone takes the time to answer it please.
I’m trying to build a custom loss function, and for now as a dummy I’m just trying to build a MSE function and compare it with the in-built MSE.
My code is just an autoencoder that receives 2D images with a batch size of 128, so when verify y_true I obtain a tensor like this: [128, 256, 256] where the 128 is batch size and the other two are the dimensions.
So, when I was looking for the MSE custom loss and compared it with the in-built one, I realised that they’re doing something like this:
diff = math_ops.squared_difference(y_pred, y_true) loss = K.mean(diff, axis=-1) loss = loss/10
Then I get a vector as a loss function as this: [128,256], so my question is: is this right? shouldn’t loss be an scalar value instead of a vector?, should I use the whole 3D tensor instead of only 2 components in the 2nd line?
I’m kinda lost and since I don’t understand this I cannot move forward on my project.