sudharsan13296 / hands-on-meta-learning-with-python Goto Github PK

The directory path in cell 13 of prototypical network omniglot demo (link) is wrong, it is missing "images" directory. As a result, the output of training is wrong (as can be seen in the notebook, the model is not learning at all during training since the train_data are all zero arrays due to it failing to load from the wrong directory. I was able to train the model correctly after fixing the wrong directory path in cell 13

The AT & T's the Database of Faces (https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html) can't be downloaded, because the DTG group have be dissolved in September 2023 with his retirement. How to use the dataset now?

It seems that all the source codes assume tensorflow1.x. Any plan to upgrade to tf2?

Hello @sudharsan13296

Thank you for this very interesting work.

I have a question regarding section 6.3 "MAML in Supervised Learning".
While in Supervised learning setting, Step 3: (inner loop) is quite obvious, I'm still not sure how to implement it for Reinforcement learning setting. In fact Di consists of K trajectories each one of horizon H. How should theta'i be computed?

A- For each of the Ks trajectories?
B- At the end of the all Ks trajectories training?

In both cases, do you have an idea on how should gradient-descent/losses be operated (eventually aggregated) to obtain theta'i?

Best Regards,

Hello @sudharsan13296,

I ordered your book " Hands On Meta Learning With-Python" one month ago, but I haven't been delivered yet. Unfortunately this github version is still missing some sections. Do you have any idea about the book finalization and availability to the public?

Best Regards,