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") }