Purpose & architecture
StyleGAN2 is local Generator Core registry configuration 31, created by NewStyleGAN2 in the adversarial package. Its exact constructor and configuration settings are preserved below.
The shared implementation initializes separate generator and discriminator arrays, maps a latent vector through the generator and scores the result with the discriminator. Train computes logarithmic discriminator and generator losses from real and generated inputs.
The Train method returns a loss without updating the two networks. Flags such as wasserstein or gradient_penalty do not by themselves demonstrate the corresponding loss or constraint; the inspected shared loss remains logarithmic. These are configurations of this local family code.
This source generation
This record follows the claude/improve-math-logic-ehIW1 branch of Janady13/complete-agi-core at commit 33aa84f2aa61cedbe010240ada7f4efbdfe795a7. The snapshot identifies the source inspected in this audit. It is not a statement about which revision is currently running on a device.
| Field | Source record |
|---|---|
| Revision | 33aa84f2aa61cedbe010240ada7f4efbdfe795a7 |
| Branch | claude/improve-math-logic-ehIW1 |
| Recorded generation | Generator Core · adversarial |
Recovered implementation structure
These paths locate the components and entry points represented by this build. The source register contains their inspected revisions.
- generator_core/pkg/generators/adversarial/adversarial.go
Behavior & verification limits
The Train method returns a loss without updating the two networks. Flags such as wasserstein or gradient_penalty do not by themselves demonstrate the corresponding loss or constraint; the inspected shared loss remains logarithmic. These are configurations of this local family code.
Original project documentation
The following documentation is preserved as a dated source account. Claims in it retain the source’s wording and must be read alongside the implementation notes above.
func NewStyleGAN2() *AdversarialGenerator {
g := NewAdversarialGenerator(31, "StyleGAN2", 512)
g.Config().Params["style_dim"] = 512
g.Config().Params["path_regularization"] = true
return g
}Equations in this build
1 linked mathematical records. Each opens its notation, interpretation and source.