Purpose & architecture
The shared implementation encodes latent means and log variances, samples with reparameterization, decodes the sample and computes a Gaussian KL term. Train combines binary cross-entropy reconstruction loss with a configurable beta multiplier on KL divergence.
Train returns the objective without an optimizer update. Named configurations such as VQ-VAE add parameter flags to the shared variational implementation; their names alone do not supply a vector-quantization codebook or the complete original architecture.
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 · variational |
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/variational/variational.go
Behavior & verification limits
Train returns the objective without an optimizer update. Named configurations such as VQ-VAE add parameter flags to the shared variational implementation; their names alone do not supply a vector-quantization codebook or the complete original architecture.
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 variational implements variational generators (41-55) ELBO = E_q[log p(x|z)] - D_KL(q(z|x) || p(z)) ═══════════════════════════════════════════════════════════════════════════════ VARIATIONAL GENERATOR BASE ═══════════════════════════════════════════════════════════════════════════════ VariationalGenerator implements VAE-style generators NewVariationalGenerator creates a new variational generator Initialize initializes the variational generator
Equations in this build
4 linked mathematical records. Each opens its notation, interpretation and source.
variational · ELBO
This relation defines ELBO in the source section “variational · ELBO”. It specifies the displayed relationship between the named quantities or parameters. Its interpretation…
func (g *VariationalGenerator) encode(x []float64) ([]float64, []float64) · reparameterize performs reparameterization trick: z
This relation defines reparameterize performs reparameterization trick: z in the source section “func (g *VariationalGenerator) encode(x []float64) ([]float64, []float64) ·…
func (g *VariationalGenerator) klDivergence(mu, logVar []float64) float64 · KL
This relation defines KL in the source section “func (g *VariationalGenerator) klDivergence(mu, logVar []float64) float64 · KL”. Uses logarithmic scaling; its base and the…
func (g *VariationalGenerator) Train(input, target []float64) (float64, error) · ELBO
This relation defines ELBO in the source section “func (g *VariationalGenerator) Train(input, target []float64) (float64, error) · ELBO”. It specifies the displayed relationship…