func randomIntInRange(min, max int) int {
if min == max {
logg.Log("warn: min==max (%v == %v)", min, max)
return min
}
return rand.Intn(max-min) + min
}
func uploadLauncher() {
openOcrUrl := fmt.Sprintf("%s:%d", *ocrUrl, *ocrPort)
openOcrClient := ocrclient.NewHttpClient(openOcrUrl)
file, err := os.Open(*ocrFile)
reader := bufio.NewReader(file)
ocrRequest := ocrclient.OcrRequest{
EngineType: ocrclient.ENGINE_TESSERACT,
InplaceDecode: false, // decode in place rather than using rabbitmq
}
ocrDecoded, err := openOcrClient.DecodeImageReader(reader, ocrRequest)
logg.Log("results: %s", ocrDecoded)
logg.Log("err: %v", err)
}
func RunStochasticHillClimber() {
ng.SeedRandom()
// training set -- todo: examples := ng.XnorTrainingSamples()
examples := ng.XnorTrainingSamples()
// create netwwork with topology capable of solving XNOR
cortex := ng.XnorCortexUntrained()
// 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: 2000,
MaxAttempts: 2000,
WeightSaturationRange: []float64{-100 * math.Pi, 100 * math.Pi},
}
cortexTrained, _, succeeded := shc.TrainExamples(cortex, examples)
if !succeeded {
panic("could not train neural net")
}
// verify it can now solve the training set
verified = cortexTrained.Verify(examples)
if !verified {
panic("could not verify neural net")
}
logg.LogTo("DEBUG", "trained cortex: %v", cortexTrained)
logg.Log("done")
}
