IEnumerator PlotCostWhenComplete(Noedify_Solver solver, float[] cost)
{
while (solver.trainingInProgress)
{
yield return(null);
}
debugger.PlotCost(cost, new float[2] {
1.0f / no_epochs * 2.5f, 5
}, costPlotOrigin);
for (int n = 0; n < predictionTester.sampleImagePlanes.Length; n++)
{
float[,,] testInputImage = new float[1, 1, 1];
Noedify_Utils.ImportImageData(ref testInputImage, predictionTester.sampleImageRandomSet[n], true);
solver.Evaluate(net, testInputImage, Noedify_Solver.SolverMethod.MainThread);
int prediction = Noedify_Utils.ConvertOneHotToInt(solver.prediction);
predictionTester.CNN_predictionText[n].text = prediction.ToString();
}
}
public void TrainModel()
{
List <float[, , ]> trainingData = new List <float[, , ]>();
List <float[]> outputData = new List <float[]>();
List <Texture2D[]> MNIST_images = new List <Texture2D[]>();
MNIST_images.Add(MNIST_images0);
MNIST_images.Add(MNIST_images1);
MNIST_images.Add(MNIST_images2);
MNIST_images.Add(MNIST_images3);
MNIST_images.Add(MNIST_images4);
MNIST_images.Add(MNIST_images5);
MNIST_images.Add(MNIST_images6);
MNIST_images.Add(MNIST_images7);
MNIST_images.Add(MNIST_images8);
MNIST_images.Add(MNIST_images9);
Noedify_Utils.ImportImageData(ref trainingData, ref outputData, MNIST_images, true);
debugger.net = net;
Noedify_Solver.SolverMethod solverMethod = Noedify_Solver.SolverMethod.MainThread;
if (solverMethodToggle != null)
{
if (solverMethodToggle.isOn)
{
solverMethod = Noedify_Solver.SolverMethod.Background;
}
}
if (solver == null)
{
solver = Noedify.CreateSolver();
}
solver.debug = new Noedify_Solver.DebugReport();
sw.Start();
//solver.costThreshold = 0.01f; // Add a cost threshold to prematurely end training when a suitably low error is achieved
//solver.suppressMessages = true; // suppress training messages from appearing in editor the console
solver.TrainNetwork(net, trainingData, outputData, no_epochs, batch_size, trainingRate, costFunction, solverMethod, null, 8);
float[] cost = solver.cost_report;
StartCoroutine(PlotCostWhenComplete(solver, cost));
}
