// firstBitTest builds a neural network to:
// - output 0 for inputs starting with a 1
// - output 1 for inputs starting with a 0.
func firstBitTest() {
trainingSamples := make([]linalg.Vector, FirstBitTrainingSize)
trainingOutputs := make([]linalg.Vector, FirstBitTrainingSize)
for i := range trainingSamples {
trainingSamples[i] = make(linalg.Vector, FirstBitInputSize)
for j := range trainingSamples[i] {
trainingSamples[i][j] = float64(rand.Intn(2))
}
trainingOutputs[i] = []float64{1 - trainingSamples[i][0]}
}
samples := neuralnet.VectorSampleSet(trainingSamples, trainingOutputs)
network := neuralnet.Network{
&neuralnet.DenseLayer{
InputCount: FirstBitInputSize,
OutputCount: FirstBitHiddenSize,
},
&neuralnet.Sigmoid{},
&neuralnet.DenseLayer{
InputCount: FirstBitHiddenSize,
OutputCount: 1,
},
&neuralnet.Sigmoid{},
}
network.Randomize()
batcher := &neuralnet.SingleRGradienter{
Learner: network,
CostFunc: neuralnet.MeanSquaredCost{},
}
sgd.SGD(batcher, samples, 0.2, 100000, 1)
var totalError float64
var maxPossibleError float64
for i := 0; i < 50; i++ {
sample := make([]float64, FirstBitInputSize)
for j := range sample {
sample[j] = float64(rand.Intn(2))
}
result := network.Apply(&autofunc.Variable{sample})
output := result.Output()[0]
amountError := math.Abs(output - (1 - sample[0]))
totalError += amountError
maxPossibleError += 1.0
}
fmt.Printf("firstBitTest() error rate: %f\n", totalError/maxPossibleError)
}
func runHorizontalLineTest(name string, network neuralnet.Network) {
trainingSamples := make([]linalg.Vector, GridTrainingSize)
trainingOutputs := make([]linalg.Vector, GridTrainingSize)
for i := range trainingSamples {
trainingSamples[i] = randomBitmap()
if bitmapHasHorizontal(trainingSamples[i]) {
trainingOutputs[i] = []float64{1}
} else {
trainingOutputs[i] = []float64{0}
}
}
samples := neuralnet.VectorSampleSet(trainingSamples, trainingOutputs)
network.Randomize()
batcher := &neuralnet.SingleRGradienter{
Learner: network,
CostFunc: neuralnet.MeanSquaredCost{},
}
sgd.SGD(batcher, samples, 0.1, 1000, 100)
var trainingError float64
var maxTrainingError float64
for i, sample := range trainingSamples {
result := network.Apply(&autofunc.Variable{sample})
output := result.Output()[0]
amountError := math.Abs(output - trainingOutputs[i][0])
trainingError += amountError
maxTrainingError += 1.0
}
var totalError float64
var maxPossibleError float64
for i := 0; i < 50; i++ {
sample := randomBitmap()
var expected float64
if bitmapHasHorizontal(sample) {
expected = 1
}
result := network.Apply(&autofunc.Variable{sample})
output := result.Output()[0]
amountError := math.Abs(output - expected)
totalError += amountError
maxPossibleError += 1.0
}
fmt.Printf("%s() training error: %f; cross error: %f\n", name,
trainingError/maxTrainingError, totalError/maxPossibleError)
}
func trainClassifier(n neuralnet.Network, d mnist.DataSet) {
log.Println("Training classifier (ctrl+C to finish)...")
killChan := make(chan struct{})
go func() {
c := make(chan os.Signal, 1)
signal.Notify(c, os.Interrupt)
<-c
signal.Stop(c)
fmt.Println("\nCaught interrupt. Ctrl+C again to terminate.")
close(killChan)
}()
inputs := make([]linalg.Vector, len(d.Samples))
outputs := make([]linalg.Vector, len(d.Samples))
for i, x := range d.IntensityVectors() {
inputs[i] = x
}
for i, x := range d.LabelVectors() {
outputs[i] = x
}
samples := neuralnet.VectorSampleSet(inputs, outputs)
batcher := &neuralnet.BatchRGradienter{
Learner: n.BatchLearner(),
CostFunc: neuralnet.MeanSquaredCost{},
}
crossValidation := mnist.LoadTestingDataSet()
sgd.SGDInteractive(batcher, samples, ClassifierStepSize,
ClassifierBatchSize, func() bool {
printScore("Training", n, d)
printScore("Cross", n, crossValidation)
return true
})
}
func Autoencode(images <-chan image.Image) (neuralnet.Network, error) {
firstImage := <-images
if firstImage == nil {
return nil, errors.New("no readable images")
}
width := firstImage.Bounds().Dx()
height := firstImage.Bounds().Dy()
log.Print("Reading images...")
tensors := []*neuralnet.Tensor3{ImageTensor(firstImage)}
for img := range images {
if img.Bounds().Dx() != width || img.Bounds().Dy() != height {
log.Printf("Image size %d,%d does not match %d,%d",
img.Bounds().Dx(), img.Bounds().Dy(),
width, height)
} else {
tensors = append(tensors, ImageTensor(img))
}
}
log.Print("Training network (ctrl+c to finish)...")
tensorSlices := make([]linalg.Vector, len(tensors))
for i, tensor := range tensors {
tensorSlices[i] = tensor.Data
}
samples := neuralnet.VectorSampleSet(tensorSlices, tensorSlices)
average, stddev := statisticalInfo(tensorSlices)
network := neuralnet.Network{
&neuralnet.RescaleLayer{
Bias: -average,
Scale: 1 / stddev,
},
&neuralnet.DenseLayer{
InputCount: width * height * 3,
OutputCount: HiddenSize1,
},
neuralnet.Sigmoid{},
&neuralnet.DenseLayer{
InputCount: HiddenSize1,
OutputCount: HiddenSize2,
},
neuralnet.Sigmoid{},
&neuralnet.DenseLayer{
InputCount: HiddenSize2,
OutputCount: HiddenSize1,
},
neuralnet.Sigmoid{},
&neuralnet.DenseLayer{
InputCount: HiddenSize1,
OutputCount: width * height * 3,
},
}
network.Randomize()
ui := hessfree.NewConsoleUI()
learner := &hessfree.DampingLearner{
WrappedLearner: &hessfree.NeuralNetLearner{
Layers: network,
Output: nil,
Cost: neuralnet.SigmoidCECost{},
MaxSubBatch: MaxSubBatch,
MaxConcurrency: 2,
},
DampingCoeff: 2,
UI: ui,
}
trainer := hessfree.Trainer{
Learner: learner,
Samples: samples,
BatchSize: samples.Len(),
UI: ui,
Convergence: hessfree.ConvergenceCriteria{
MinK: 5,
},
}
trainer.Train()
network = append(network, neuralnet.Sigmoid{})
return network, nil
}
func dataSetSamples(d mnist.DataSet) sgd.SampleSet {
labelVecs := d.LabelVectors()
inputVecs := d.IntensityVectors()
return neuralnet.VectorSampleSet(vecVec(inputVecs), vecVec(labelVecs))
}