Dear everybody:
Usually, we separate input data into three, which are training set, validation set, test set in deep learning.
For each epoch, I want to do the best way to get a better model using validation set.
For example, for each epoch, after finishing learning with training set, I can select the model parameter which has the lowest loss w.r.t. validation set by saving the model parameter each time when the loss w.r.t. validation set has lower value than the
lowest loss by then.
Also, I can select the lowest validation loss while using different validation set each time
by mixing training set and validation set and randomly selecting some fraction of the mixture as a new
validation set
Is this the best or at least recommended way to get a better or the best model parameter???
I will appreciate if you can help me with this problem
Thank you