28. layers/metacognition

Part: III · Layers
Package: github.com/openfluke/welvet/layers/metacognition
Status: ok — ✅

When

Building or training a net that needs the layers/metacognition Op (also usable as a Parallel cam).

Where

import "github.com/openfluke/welvet/layers/metacognition"

cd 28-metacognition && source ../env.sh && go run .

Why

Observed layers can apply heuristic stability rules (gate/scale/reset) without dtype morph/QAT.

What

Wraps Dense + DefaultStabilityRules(); Stats exposed. Full timed matrix + train grids.

Sample output (captured)

16 <nil>

Live capture from go run ./cmd/runall on local ../../welvet (exit 0).

Source

Copied from the Welvet feature book examples (openfluke.github.io/welvet/examples/28-metacognition).

package main

import (
	"fmt"

	"github.com/openfluke/welvet/core"
	"github.com/openfluke/welvet/layers/metacognition"
)

func main() {
	l, err := metacognition.New(metacognition.Config{
		Dim: 16, Rules: metacognition.DefaultStabilityRules(),
	})
	if err != nil {
		panic(err)
	}
	x := core.NewTensor[float32](1, 16)
	_, y, err := metacognition.Forward(l, x)
	fmt.Println(len(y.Data), err)
}