void Update() { if (Input.GetKeyDown(KeyCode.A)) { if (!isClassification) { predictMLPMulticlassRegression(); } else { predictMLPMulticlass(); } } if (Input.GetKeyDown(KeyCode.T)) { print("Starting trainning"); MlDllWrapper.trainMLPModelClass(MyModel, numberLayer, trainningInput.Length / npl[0], npl, trainningInput, trainningInput.Length, _inputSize, trainningOuput, trainningOuput.Length, _outputSize, epochs, learningRate, isClassification); print("Trainning Finished"); //trainModel(); //trainLinearMulticlass(); } if (Input.GetKeyDown(KeyCode.R)) { MlDllWrapper.DeleteLinearModel(MyModel); } }
void Update() { if (Input.GetKeyDown(KeyCode.A)) { predictMLPMulticlass(); } if (Input.GetKeyDown(KeyCode.I)) { File.Copy(userRequestPathFile, @".\Assets\Resources\Dataset\requestImage.jpg", true); requestPicRead(); } if (Input.GetKeyDown(KeyCode.T)) { print("Starting trainning"); MlDllWrapper.trainMLPModelClass(MyModel, numberLayer, trainningInput.Length / npl[0], npl, trainningInput, trainningInput.Length, _inputSize, trainningOuput, trainningOuput.Length, _outputSize, epochs, learningRate, isClassification); evaluateDataset(); print("Trainning Finished"); } if (Input.GetKeyDown(KeyCode.R)) { MlDllWrapper.DeleteLinearModel(MyModel); } if (Input.GetKeyDown(KeyCode.O)) { StopAllCoroutines(); StartCoroutine(infiniteTrain()); } }