Purpose & architecture
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.
Package adversarial implements adversarial generators (26-40) min_G max_D V(D,G) = E[log D(x)] + E[log(1-D(G(z)))] ═══════════════════════════════════════════════════════════════════════════════ ADVERSARIAL GENERATOR BASE ═══════════════════════════════════════════════════════════════════════════════ AdversarialGenerator implements GAN-style generators NewAdversarialGenerator creates a new adversarial generator Initialize initializes the adversarial generator
Equations in this build
1 linked mathematical records. Each opens its notation, interpretation and source.