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());
}
}
