I have a quite big dataset of 10000 training data, i held out 2000 points for validation.
I am using a Convolutional Neural Network and using minibatch stochastic gradient descent to minimize the RMS error, with minibatch size of 50,i am training for 1000 epochs.
The training loss in the final epoch turns out to be 2.72 (averaging over the minibatch (size 50)).
However the validation loss comes out much lesser, 0.33 to be precise (averaging over the 2000 validation points).
How do i interpret these values, i am thinking that averaging over 2000 values compared to only 50 in the minibatch is playing a part.