HerbDev model-development proof of concept

Generative Adversarial Network (GAN)

This case study shows the complete model-training workflow: establish a baseline, inspect evidence, make a controlled optimization, rerun the model, and preserve the resulting artifacts.

Baseline

generated_mean	3.20711
target_mean	2.0
mean_error	1.20711
generated_std	1.3094
target_std	0.6
train_seconds	0.37638

Optimization decision

The baseline adversarial game was unstable. Lower the learning rate, increase capacity, and give both networks more update steps before judging distribution fit.

Optimized run

generated_mean	1.95252
target_mean	2.0
mean_error	0.04748
generated_std	0.61628
target_std	0.6
train_seconds	1.34144

Training evidence

Baseline and optimized training loss comparison

Execution environment

platform	Linux-6.18.35-x86_64-with-glibc2.41
machine	x86_64
python	3.13.5
torch	2.10.0+cpu
device	cpu
mps_available	False
cuda_available	False
seed	42

What this demonstrates for a client

Model development is not a single training command. The useful work is creating a reproducible dataset, choosing a defensible baseline, measuring behavior, changing one thing for a reason, retesting, and preserving enough evidence to explain what happened. The same process scales to client data, larger models, and production infrastructure.

Have a model or dataset that needs improvement?

The next step is to define the business outcome, available data, constraints, and success metric, then design the smallest experiment that can prove whether machine learning is worth pursuing.