Chapter 26

layers/kmeans

github.com/openfluke/welvet/layers/kmeans🚧

Why it exists

Soft clustering as a differentiable layer lets topology experiments sit inside the same train loop.

What it is

Centers on Dense (K×FeatureDim); soft assignment outputs. Smoke+census.

Go example

examples/26-kmeans/main.go

Run:cd welvet/examples/26-kmeans && source ../env.sh && go run .

package main

import (
	"fmt"

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

func main() {
	l, err := kmeans.New(kmeans.Config{NumClusters: 4, FeatureDim: 8})
	if err != nil {
		panic(err)
	}
	x := core.NewTensor[float32](1, 8)
	_, y, err := kmeans.Forward(l, x)
	fmt.Println(y.Shape, err)
}

Output

exit 0 · last run via go run .

[1 4] <nil>