Gin Config + PyTorch: Fixed code, configurable experiments
Tutorial on moving experiment variables out of source code into .gin files, keeping the training loop static.
• Scoped MLP variants with configurable architecture
• Optimizer, scheduler, loss, batching exposed via @gin.configurable
• Runtime overrides without editing source
• Operative config export per run for reproducibility