func main() {
training := mnist.LoadTrainingDataSet()
samples := make([][]bool, len(training.Samples))
for i, sample := range training.Samples {
samples[i] = make([]bool, len(sample.Intensities))
for j, x := range sample.Intensities {
if x > 0.5 {
samples[i][j] = true
}
}
}
layers := buildLayers()
trainer := rbm.Trainer{
GibbsSteps: GibbsSteps,
StepSize: BigStepSize,
Epochs: BigEpochs,
BatchSize: runtime.GOMAXPROCS(0),
}
log.Println("Training...")
trainer.TrainDeep(layers, samples[:1000])
trainer.StepSize = SmallStepSize
trainer.Epochs = SmallEpochs
trainer.TrainDeep(layers, samples[:1000])
log.Println("Generating outputs...")
testingSamples := mnist.LoadTestingDataSet()
mnist.SaveReconstructionGrid("output.png", func(img []float64) []float64 {
return reconstruct(layers, img)
}, testingSamples, ReconstructionGridSize, ReconstructionGridSize)
}
func trainingSamples() []idtrees.Sample {
set := mnist.LoadTrainingDataSet()
res := make([]idtrees.Sample, len(set.Samples))
for i, x := range set.Samples {
res[i] = newImageSample(x)
}
return res
}
func main() {
training := mnist.LoadTrainingDataSet()
crossValidation := mnist.LoadTestingDataSet()
net := createNet(training)
trainingSamples := dataSetSamples(training)
gradienter := &neuralnet.BatchRGradienter{
Learner: net.BatchLearner(),
CostFunc: neuralnet.MeanSquaredCost{},
}
rmsGrad := &sgd.RMSProp{Gradienter: gradienter}
sgd.SGDInteractive(rmsGrad, trainingSamples, StepSize, BatchSize, func() bool {
log.Println("Printing score...")
printScore("Cross", net, crossValidation)
log.Println("Running training round...")
return true
})
}
func main() {
if len(os.Args) != 2 {
fmt.Fprintln(os.Stderr, "Usage: mnist_classify <classifier_out.json>")
os.Exit(1)
}
outputFile := os.Args[1]
training := mnist.LoadTrainingDataSet()
binSamples := binarySamples(training.Samples)
classifier := pretrainedClassifier(binSamples)
trainClassifier(classifier, training)
data, _ := classifier.Serialize()
if err := ioutil.WriteFile(outputFile, data, 0755); err != nil {
fmt.Fprintln(os.Stderr, err)
os.Exit(1)
}
}