Hi @xchoo, thanks for your help! I’ve tried implementing all of the steps you mentioned but I think I’m still doing something wrong. To troubleshoot, I’ve left the LMU cell as it is and only made the changes you list here (so I’m not creating that sort of “bottleneck” an autoencoder has). Right now I just want to see if I can get the more basic setup working.
I think my problem is with the data I use for sim.fit. Right now I’ve got it like this:
sim.fit(train_images, train_images, epochs=1)
where train_images.shape is (784,1). When I do this my network doesn’t seem to learn anything, the reconstructed images are just noise. Based on what you said above though, I thought this should be the right shape for the data. Am I missing something here? I’m having trouble figuring out what else the problem could be since I didn’t change much else from the original LMU example. As you said, I’m using mse as my loss function and Adam as the optimizer with a learning rate of 0.001.