func TestMutatorsThatAlwaysMutate(t *testing.T) {
testCortex := BasicCortex()
cortexMutators := []CortexMutator{
RemoveBias,
MutateWeights,
ResetWeights,
MutateActivation,
AddInlinkRecurrent,
AddInlinkNonRecurrent,
AddOutlinkRecurrent,
AddOutlinkNonRecurrent,
AddNeuronNonRecurrent,
AddNeuronRecurrent,
OutspliceRecurrent,
OutspliceNonRecurrent,
}
for _, cortexMutator := range cortexMutators {
beforeString := ng.JsonString(testCortex)
ok, _ := cortexMutator(testCortex)
assert.True(t, ok)
afterString := ng.JsonString(testCortex)
hasChanged := beforeString != afterString
if !hasChanged {
log.Printf("!hasChanged. beforeString/afterString: %v", beforeString)
}
assert.True(t, hasChanged)
if !hasChanged {
break
}
}
}
func TestAddBias(t *testing.T) {
xnorCortex := ng.XnorCortex()
for _, neuron := range xnorCortex.Neurons {
neuron.Bias = 0.0
}
beforeString := ng.JsonString(xnorCortex)
AddBias(xnorCortex)
afterString := ng.JsonString(xnorCortex)
assert.True(t, beforeString != afterString)
}
func RunTopologyMutatingTrainer() bool {
ng.SeedRandom()
// training set
examples := ng.XnorTrainingSamples()
// create netwwork with topology capable of solving XNOR
cortex := ng.BasicCortex()
// verify it can not yet solve the training set (since training would be useless in that case)
verified := cortex.Verify(examples)
if verified {
panic("neural net already trained, nothing to do")
}
shc := &nv.StochasticHillClimber{
FitnessThreshold: ng.FITNESS_THRESHOLD,
MaxIterationsBeforeRestart: 20000,
MaxAttempts: 10,
WeightSaturationRange: []float64{-10000, 10000},
}
tmt := &nv.TopologyMutatingTrainer{
MaxAttempts: 100,
MaxIterationsBeforeRestart: 5,
StochasticHillClimber: shc,
}
cortexTrained, succeeded := tmt.TrainExamples(cortex, examples)
if succeeded {
logg.LogTo("MAIN", "Successfully trained net: %v", ng.JsonString(cortexTrained))
// verify it can now solve the training set
verified = cortexTrained.Verify(examples)
if !verified {
logg.LogTo("MAIN", "Failed to verify neural net")
succeeded = false
}
}
if !succeeded {
logg.LogTo("MAIN", "Failed to train neural net")
}
return succeeded
}