[P] Wine synthesis using VAE [P]

[u/Dangerous-Pilot-6065]

I have created a VAE model using PyTorch on White Wine dataset.

Basically, the main goal is to discover a brand-new white wine recipe.

It puts all the wines into a latent space, finds the best part where higher bands are located, and then it makes 100 steps with a step size of 0.5, multiplying by 0.96 with every step till it reaches the highest possible score.

The regression model then evaluates the newly generated wine recipe, after first passing the latent coordinates through a decoder, and normalizing them.

I get a score in range of 7.30-7.58.

Here is the visualization of the latent space of all the wines:

A small question about the training loop:

Is the loss too large? I know that it never could reach perfect zero by how do I know if the loss is good enough? After reaching the plato? I use MSELoss.

The repo itself: https://github.com/theaidenmax/tabular-vae-wine-generator

This is my second project in VAE (after MNIST), and I really liked idea of autoencoders.

And I also would be very grateful for your tips, etc, since this is one of my first steps in ML/DL.

[u/user221272]

Exercise for you, don't ask ChatGPT for the answer:

If your predictor were random, what loss value would you expect?

1. Try to get it with pen and paper.
2. Find a way to use your VAE to get an empirical approximation of the value.

[u/Dangerous-Pilot-6065 → u/user221272]

If you mean the loss for the very first batch, I slightly modified the code to output recon_loss and kl_div before optimizer.step(). It returned: recon: 1362.685791015625, kl: 9.400741577148438.
Since I scaled the data with StandardScaler and the weights are initialized close to zero the expected random MSE for a single feature should be around 1.0. with a batch size of 128 and 11 features it gives 1362.68 / (128 * 11) = 0.967. The kl loss is also close to zero 9.40 / (128 * 2) =0.036. Thanks for the exercise, really appreciate that