func (shc *StochasticHillClimber) Train(cortex *ng.Cortex, scape Scape) (resultNeuralNet *ng.Cortex, fitness float64, succeeded bool) {
shc.validate()
numAttempts := 0
fittestNeuralNet := cortex.Copy()
resultNeuralNet = cortex
// Apply NN to problem and save fitness
fitness = scape.Fitness(fittestNeuralNet)
logg.LogTo("MAIN", "Initial fitness: %v", fitness)
if fitness > shc.FitnessThreshold {
succeeded = true
return
}
for i := 0; ; i++ {
// Save the genotype
candidateNeuralNet := fittestNeuralNet.Copy()
// Perturb synaptic weights and biases
PerturbParameters(candidateNeuralNet, shc.WeightSaturationRange)
// Re-Apply NN to problem
candidateFitness := scape.Fitness(candidateNeuralNet)
logg.LogTo("DEBUG", "candidate fitness: %v", fitness)
// If fitness of perturbed NN is higher, discard original NN and keep new
// If fitness of original is higher, discard perturbed and keep old.
if candidateFitness > fitness {
logg.LogTo("MAIN", "i: %v candidateFitness: %v > fitness: %v", i, candidateFitness, fitness)
i = 0
fittestNeuralNet = candidateNeuralNet
resultNeuralNet = candidateNeuralNet.Copy()
fitness = candidateFitness
}
if candidateFitness > shc.FitnessThreshold {
logg.LogTo("MAIN", "candidateFitness: %v > Threshold. Success at i=%v", candidateFitness, i)
succeeded = true
break
}
if ng.IntModuloProper(i, shc.MaxIterationsBeforeRestart) {
logg.LogTo("MAIN", "** restart hill climber. fitness: %f i/max: %d/%d", fitness, numAttempts, shc.MaxAttempts)
numAttempts += 1
i = 0
shc.resetParametersToRandom(fittestNeuralNet)
ng.SeedRandom()
}
if numAttempts >= shc.MaxAttempts {
succeeded = false
break
}
}
return
}
func (tmt *TopologyMutatingTrainer) Train(cortex *ng.Cortex, scape Scape) (fittestCortex *ng.Cortex, succeeded bool) {
ng.SeedRandom()
shc := tmt.StochasticHillClimber
includeNonTopological := false
mutators := CortexMutatorsNonRecurrent(includeNonTopological)
originalCortex := cortex.Copy()
currentCortex := cortex
// Apply NN to problem and save fitness
logg.LogTo("MAIN", "Get initial fitness")
fitness := scape.Fitness(currentCortex)
logg.LogTo("MAIN", "Initial fitness: %v", fitness)
if fitness > shc.FitnessThreshold {
succeeded = true
return
}
for i := 0; ; i++ {
logg.LogTo("MAIN", "Before mutate. i/max: %d/%d", i, tmt.MaxAttempts)
// before we mutate the cortex, we need to init it,
// otherwise things like Outsplice will fail because
// there are no DataChan's.
currentCortex.Init()
// mutate the network
randInt := RandomIntInRange(0, len(mutators))
mutator := mutators[randInt]
ok, _ := mutator(currentCortex)
if !ok {
logg.LogTo("MAIN", "Mutate didn't work, retrying...")
continue
}
isValid := currentCortex.Validate()
if !isValid {
logg.LogPanic("Cortex did not validate")
}
filenameJson := fmt.Sprintf("cortex-%v.json", i)
currentCortex.MarshalJSONToFile(filenameJson)
filenameSvg := fmt.Sprintf("cortex-%v.svg", i)
currentCortex.RenderSVGFile(filenameSvg)
logg.LogTo("MAIN", "Post mutate cortex svg: %v json: %v", filenameSvg, filenameJson)
logg.LogTo("MAIN", "Run stochastic hill climber..")
// memetic step: call stochastic hill climber and see if it can solve it
fittestCortex, _, succeeded = shc.Train(currentCortex, scape)
logg.LogTo("MAIN", "stochastic hill climber finished. succeeded: %v", succeeded)
if succeeded {
succeeded = true
break
}
if i >= tmt.MaxAttempts {
succeeded = false
break
}
if ng.IntModuloProper(i, tmt.MaxIterationsBeforeRestart) {
logg.LogTo("MAIN", "** Restart . i/max: %d/%d", i, tmt.MaxAttempts)
currentCortex = originalCortex.Copy()
isValid := currentCortex.Validate()
if !isValid {
currentCortex.Repair() // TODO: remove workaround
isValid = currentCortex.Validate()
if !isValid {
logg.LogPanic("Cortex could not be repaired")
}
}
}
}
return
}
