static void Main(string[] args)
{
// get RI instances predefined in the config settings
RILogManager.Get("ViewerOnly").SendMessage("Only the Viewer will get this message");
RILogManager.Get("File1Only").SendMessage("Only TextFile1 and BinaryFile1 will get this message");
RILogManager.Get("File2Only").SendMessage("Only TextFile2 and BinaryFile2 will get this message");
// by code - example 1a
using (ReflectInsight ri = new ReflectInsight("Example1"))
{
ri.SetDestinationBindingGroup("ViewerOnlyBinding");
ri.SendMessage("Only the Viewer will get this message");
}
// by code - example 1b
using (ReflectInsight ri = new ReflectInsight("Example1"))
{
ri.SetDestinationBindingGroup("File1OnlyBinding");
ri.SendMessage("Only TextFile1 and BinaryFile1 will get this message");
}
// you can create instances on the fly and used them throughout the application as needed
RILogManager.Add("AnotherViewerOnlyInstance", "Another Category", String.Empty, "ViewerOnlyBinding");
RILogManager.Add("AnotherFile1OnlyInstance", "Another Category", String.Empty, "File1OnlyBinding");
RILogManager.Get("AnotherViewerOnlyInstance").SendMessage("Only the Viewer will get this message");
RILogManager.Get("AnotherFile1OnlyInstance").SendMessage("Only TextFile1 and BinaryFile1 will get this message");
}
// MNIST accuracy tester
public static void Run(double accuracyThreshold = .9979D)
{
MnistData mnistData = new MnistData(28);
Real maxAccuracy = 0;
//Number of middle layers
const int N = 30; //It operates at 1000 similar to the reference link but it is slow at the CPU
ReflectInsight ri = new ReflectInsight("Test21");
ri.Enabled = true;
RILogManager.Add("Test21", "Test21");
RILogManager.SetDefault("Test21");
//FunctionStack nn = new FunctionStack("Test21",
// new Linear(28 * 28, 1024, name: "l1 Linear"),
// new Sigmoid(name: "l1 Sigmoid"),
// new Linear(1024, 10, name: "l2 Linear")
//);
//nn.SetOptimizer(new MomentumSGD());
FunctionStack nn = new FunctionStack("Test7",
new Linear(true, 28 * 28, N, name: "l1 Linear"), // L1
new BatchNormalization(true, N, name: "l1 BatchNorm"),
new ReLU(name: "l1 ReLU"),
new Linear(true, N, N, name: "l2 Linear"), // L2
new BatchNormalization(true, N, name: "l2 BatchNorm"),
new ReLU(name: "l2 ReLU"),
new Linear(true, N, N, name: "l3 Linear"), // L3
new BatchNormalization(true, N, name: "l3 BatchNorm"),
new ReLU(name: "l3 ReLU"),
new Linear(true, N, N, name: "l4 Linear"), // L4
new BatchNormalization(true, N, name: "l4 BatchNorm"),
new ReLU(name: "l4 ReLU"),
new Linear(true, N, N, name: "l5 Linear"), // L5
new BatchNormalization(true, N, name: "l5 BatchNorm"),
new ReLU(name: "l5 ReLU"),
new Linear(true, N, N, name: "l6 Linear"), // L6
new BatchNormalization(true, N, name: "l6 BatchNorm"),
new ReLU(name: "l6 ReLU"),
new Linear(true, N, N, name: "l7 Linear"), // L7
new BatchNormalization(true, N, name: "l7 BatchNorm"),
new ReLU(name: "l7 ReLU"),
new Linear(true, N, N, name: "l8 Linear"), // L8
new BatchNormalization(true, N, name: "l8 BatchNorm"),
new ReLU(name: "l8 ReLU"),
new Linear(true, N, N, name: "l9 Linear"), // L9
new BatchNormalization(true, N, name: "l9 BatchNorm"),
new ReLU(name: "l9 ReLU"),
new Linear(true, N, N, name: "l10 Linear"), // L10
new BatchNormalization(true, N, name: "l10 BatchNorm"),
new ReLU(name: "l10 ReLU"),
new Linear(true, N, N, name: "l11 Linear"), // L11
new BatchNormalization(true, N, name: "l11 BatchNorm"),
new ReLU(name: "l11 ReLU"),
new Linear(true, N, N, name: "l12 Linear"), // L12
new BatchNormalization(true, N, name: "l12 BatchNorm"),
new ReLU(name: "l12 ReLU"),
new Linear(true, N, N, name: "l13 Linear"), // L13
new BatchNormalization(true, N, name: "l13 BatchNorm"),
new ReLU(name: "l13 ReLU"),
new Linear(true, N, N, name: "l14 Linear"), // L14
new BatchNormalization(true, N, name: "l14 BatchNorm"),
new ReLU(name: "l14 ReLU"),
new Linear(true, N, 10, name: "l15 Linear") // L15
);
// 0.0005 - 97.5, 0.001, 0.00146
double alpha = 0.001;
double beta1 = 0.9D;
double beta2 = 0.999D;
double epsilon = 1e-8;
nn.SetOptimizer(new Adam("Adam21", alpha, beta1, beta2, epsilon));
Stopwatch sw = new Stopwatch();
sw.Start();
for (int epoch = 0; epoch < 3; epoch++)
{
Real totalLoss = 0;
long totalLossCount = 0;
for (int i = 1; i < TRAIN_DATA_COUNT + 1; i++)
{
TestDataSet datasetX = mnistData.GetRandomXSet(BATCH_DATA_COUNT, 28, 28);
Real sumLoss = Trainer.Train(nn, datasetX.Data, datasetX.Label, new SoftmaxCrossEntropy());
totalLoss = sumLoss;
totalLossCount++;
if (i % 20 == 0)
{
TestDataSet datasetY = mnistData.GetRandomYSet(TEST_DATA_COUNT, 28);
Real accuracy = Trainer.Accuracy(nn, datasetY.Data, datasetY.Label, false);
if (accuracy > maxAccuracy)
{
maxAccuracy = accuracy;
}
Passed = (accuracy >= accuracyThreshold);
sw.Stop();
ri.ViewerSendWatch("Iteration", "epoch " + (epoch + 1) + " of 3, batch " + i + " of " + TRAIN_DATA_COUNT);
ri.ViewerSendWatch("Max Accuracy", maxAccuracy * 100 + "%");
ri.ViewerSendWatch("Current Accuracy", accuracy * 100 + "%");
ri.ViewerSendWatch("Total Loss ", totalLoss / totalLossCount);
ri.ViewerSendWatch("Elapsed Time", Helpers.FormatTimeSpan(sw.Elapsed));
ri.ViewerSendWatch("Accuracy Threshold", Passed ? "Passed" : "Not Passed");
sw.Start();
}
}
sw.Stop();
ri.SendInformation("Total Processing Time: " + Helpers.FormatTimeSpan(sw.Elapsed));
}
}
