optuna VS OnGrad

I didn’t even know WandB did hyperparameter optimization, I figured it was a neural network visualizer based on 2 minute papers. Didn’t seem like many alternatives out there to Optuna with TPE + persistence in conditional continuous & discrete spaces.

Anyway, it’s doable to make a multi objective decide_to_prune function with Optuna, here’s an example https://github.com/optuna/optuna/issues/3450#issuecomment-19...

The network configuration process is usually not particularly scientific and mostly relies on empirical observation. For some cases, tools like Optuna can be used to automatically find the optimal parameters. In others, on others, you can look for modern studies which explore the effect of this parameter on performance, such as this study (2022), but these are typically very specific to one particular architecture.

Keras Tuner, Optuna : https://github.com/optuna/optuna ?

I have used OpenTuner, but I don't think it is maintained anymore. I hear tell that Optuna is what to use now, but have not used it myself. https://optuna.org Optuna - A hyperparameter optimization framework

Research the type of model to get an idea of what hyper parameters to use. I recommend using a hyper parameter optimization library like Optuna to get the best configuration

You can use Optuna, SMAC or hyperopt

I made a little algo I use for non-differentiable loss functions. The general idea is that we estimate the gradient by scoring noise in weights. Each step, instead of starting from scratch we start from near the previous gradient estimations and hopefully only calculate as many samples that are needed to "saturate" the estimate. Although it's a reinforcement algorithm, you can get score the model via your own loss function. The usage is very abstract such that you supply your own model and get/set params. The algorithm itself doesn't really care about any of that. It's worked pretty well for my use cases, feel free to give it a try- https://github.com/ben-arnao/OnGrad

I'm not sure I understand the problem completely but I've developed an alternative RL-esque algorithm to deal with weird types of problems/environments (ex. non-differentiable). Maybe this would fall into that category? It's very flexible as you really only need to "score" a set of model weights, what model you use and how you score is entirely up to you. Let me know if you find it useful! https://github.com/ben-arnao/OnGrad

That's true. I don't know if your problem is differentiable then, so standard optimizers might not work. If you're interested, I made a small derivate-free library. It's for reinforcement learning but as long as you define your own loss function for maximization instead of minimization you might still be able to use it as is. https://github.com/ben-arnao/OnGrad

That paper actually served as an inspiration for an alternative method I created as well. I use final episode score directly though because this is usually what we really care about. By using episode score we eliminate the messiness of time horizons, reward back propagation, etc. Plus a lot of these policy gradient methods usually on some level optimizing a loss function which isn't truly representative of the underlying function and is only correlated (ex. Not all problems are even differentiable at all). I'm not sure if it makes sense for all use cases but for some it is very promising. I want to keep trying with different problems and play with the config. Doesn't seem very similar to your method but I think both try to solve some of these issues https://github.com/ben-arnao/OnGrad

I've always had a bad time with PPO. Seems too complicated for it's own good and *maybe* only if you work at your issue for a while and tune hyperparameters and set things up correctly to the T you'll get some OK results. That's why I created my own RIL algo. Feel free to give it a try and let me know if it works for you. https://github.com/ben-arnao/OnGrad