partial void TappedTestSet(UIButton sender)
{
// placeholder to count number of correct detections on the test set
var correctDetections = 0;
var total = 10000f;
AccuracyLabel.Hidden = false;
Atomics.Reset();
// validate NeuralNetwork was initialized properly
if (runningNet == null)
{
throw new InvalidProgramException();
}
for (int i = 0; i < total; i++)
{
Inference(i, Mnistdata.LabelsCount);
if (i % 100 == 0)
{
AccuracyLabel.Text = $"{i / 100}% Done";
// this command helps update the UI in the loop regularly
NSRunLoop.Current.RunUntil(NSRunLoopMode.Default, NSDate.DistantPast);
}
}
// display accuracy of the network on the MNIST test set
correctDetections = Atomics.GetCount();
AccuracyLabel.Hidden = false;
AccuracyLabel.Text = $"Accuracy = {(correctDetections * 100) / total}%";
}
/// <summary>
/// This function encodes all the layers of the network into given commandBuffer, it calls subroutines for each piece of the network
/// Returns: Guess of the network as to what the digit is as uint
/// </summary>
/// <param name="inputImage">Image coming in on which the network will run</param>
/// <param name="imageNum">If the test set is being used we will get a value between 0 and 9999 for which of the 10,000 images is being evaluated</param>
/// <param name="correctLabel">The correct label for the inputImage while testing</param>
public override uint Forward(MPSImage inputImage = null, int imageNum = 9999, int correctLabel = 10)
{
uint label = 99;
// Get command buffer to use in MetalPerformanceShaders.
using (var commandBuffer = commandQueue.CommandBuffer()) {
// output will be stored in this image
var finalLayer = new MPSImage(commandBuffer.Device, DID);
// encode layers to metal commandBuffer
if (inputImage == null)
{
conv1.EncodeToCommandBuffer(commandBuffer, SrcImage, c1Image);
}
else
{
conv1.EncodeToCommandBuffer(commandBuffer, inputImage, c1Image);
}
pool.EncodeToCommandBuffer(commandBuffer, c1Image, p1Image);
conv2.EncodeToCommandBuffer(commandBuffer, p1Image, c2Image);
pool.EncodeToCommandBuffer(commandBuffer, c2Image, p2Image);
fc1.EncodeToCommandBuffer(commandBuffer, p2Image, fc1Image);
fc2.EncodeToCommandBuffer(commandBuffer, fc1Image, dstImage);
softmax.EncodeToCommandBuffer(commandBuffer, dstImage, finalLayer);
// add a completion handler to get the correct label the moment GPU is done and compare it to the correct output or return it
commandBuffer.AddCompletedHandler(buffer => {
label = GetLabel(finalLayer);
if (correctLabel == label)
{
Atomics.Increment();
}
});
// commit commandbuffer to run on GPU and wait for completion
commandBuffer.Commit();
if (imageNum == 9999 || inputImage == null)
{
commandBuffer.WaitUntilCompleted();
}
}
return(label);
}
