Debugging the meta-optimizer #12
Description
I've implemented a small binary text classification task in problems.py and util.py. I'm using a small MLP similar to the MNIST model. When I run the model with a regular optimizer, the loss on the training dataset goes down easily.
However, the meta-optimizer fails to minimize the loss; after 10k epochs, the loss is still as if the model was random. Do you have any insight or tips on how I could debug the meta-learner?
Thanks in advance. I really appreciate your help.