func printScore(prefix string, n neuralnet.Network, d mnist.DataSet) {
classifier := func(v []float64) int {
r := n.Apply(&autofunc.Variable{v})
return networkOutput(r)
}
correctCount := d.NumCorrect(classifier)
histogram := d.CorrectnessHistogram(classifier)
log.Printf("%s: %d/%d - %s", prefix, correctCount, len(d.Samples), histogram)
}
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 dataSetSamples(d mnist.DataSet) sgd.SampleSet {
labelVecs := d.LabelVectors()
inputVecs := d.IntensityVectors()
return neuralnet.VectorSampleSet(vecVec(inputVecs), vecVec(labelVecs))
}
