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random.go
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random.go
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// Copyright 2021 The rndnet Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
package main
import (
"fmt"
"math"
"sort"
"github.com/pointlander/datum/iris"
)
// RandomLayer is a random neural network layer
type RandomLayer struct {
Rows int
Columns int
Rand Rand
}
// RandomNetwork is a random neural network
type RandomNetwork []RandomLayer
// Inference performs inference on a neural network
func (n RandomNetwork) Inference(inputs, outputs []float32) {
last := len(n) - 1
for i, layer := range n {
rnd := layer.Rand
columns := len(outputs)
if i < len(n)-1 {
columns = n[i+1].Columns
}
values, factor :=
make([]float32, columns),
float32(math.Sqrt(2/float64(columns)))
for j := 0; j < layer.Rows; j++ {
sum := (2*rnd.Float32() - 1) * factor
for _, input := range inputs {
sum += input * (2*rnd.Float32() - 1) * factor
}
e := float32(math.Exp(float64(sum)))
values[j] = e / (e + 1)
}
if i == last {
copy(outputs, values)
} else {
inputs = values
}
}
}
// Copy copies a network
func (n RandomNetwork) Copy() RandomNetwork {
var network RandomNetwork
for _, layer := range n {
l := RandomLayer{
Rows: layer.Rows,
Columns: layer.Columns,
Rand: layer.Rand,
}
network = append(network, l)
}
return network
}
// RandomNetworkModel is the real network model
func RandomNetworkModel(seed int) float64 {
rnd := Rand(LFSRInit + seed)
type Genome struct {
Network RandomNetwork
Fitness float32
}
var genomes []Genome
addNetwork := func(i int) {
var network RandomNetwork
layer := RandomLayer{
Rows: 4,
Columns: 4,
Rand: Rand(LFSRInit + i + seed + NumGenomes),
}
network = append(network, layer)
layer = RandomLayer{
Rows: 3,
Columns: 4,
Rand: Rand(LFSRInit + i + seed + 2*NumGenomes),
}
network = append(network, layer)
genomes = append(genomes, Genome{
Network: network,
})
}
for i := 0; i < NumGenomes; i++ {
addNetwork(i)
}
datum, err := iris.Load()
if err != nil {
panic(err)
}
inputs, outputs := make([]float32, 4), make([]float32, 3)
get := func() int {
for {
for i, genome := range genomes {
if rnd.Float32() > genome.Fitness {
return i
}
}
}
}
i := 0
for {
for j, genome := range genomes {
sum := float32(0)
for _, flower := range datum.Fisher {
for k, value := range flower.Measures {
inputs[k] = float32(value)
}
genome.Network.Inference(inputs, outputs)
expected := make([]float32, 3)
expected[iris.Labels[flower.Label]] = 1
loss := float32(0)
for l, output := range outputs {
diff := expected[l] - output
loss += diff * diff
}
loss = float32(math.Sqrt(float64(loss)))
sum += loss
}
sum /= float32(len(datum.Fisher)) * float32(math.Sqrt(3))
genomes[j].Fitness = sum
}
sort.Slice(genomes, func(i, j int) bool {
return genomes[i].Fitness < genomes[j].Fitness
})
genomes = genomes[:NumGenomes]
i++
if i > 127 {
break
}
for i := 0; i < 256; i++ {
a, b := get(), get()
layer := rnd.Uint32() & 1
networkA, networkB :=
genomes[a].Network.Copy(), genomes[b].Network.Copy()
layerA, layerB := networkA[layer], networkB[layer]
layerA.Rand = layerA.Rand ^ layerB.Rand
genomes = append(genomes, Genome{
Network: networkA,
})
}
}
network := genomes[0].Network
misses, total := 0, 0
for _, flower := range datum.Fisher {
for k, value := range flower.Measures {
inputs[k] = float32(value)
}
network.Inference(inputs, outputs)
max, index := float32(0), 0
for j, output := range outputs {
if output > max {
max, index = output, j
}
}
if index != iris.Labels[flower.Label] {
misses++
}
total++
}
quality := float64(misses) / float64(total)
fmt.Println(genomes[0].Fitness, quality)
return quality
}