private static void Main(string[] args) {
var data = DataReader.ReadFromFile("data/iris.data")/*.OrderBy(i => random.Next())*/;
foreach(var entry in data) {
//TODO: it is suboptimal to calculate min and max every time
entry.PetalLength = Normalize(entry.PetalLength, data.Min(i => i.PetalLength), data.Max(i => i.PetalLength));
entry.PetalWidth = Normalize(entry.PetalWidth, data.Min(i => i.PetalWidth), data.Max(i => i.PetalWidth));
entry.SepalLength = Normalize(entry.SepalLength, data.Min(i => i.SepalLength), data.Max(i => i.SepalLength));
entry.SepalWidth = Normalize(entry.SepalWidth, data.Min(i => i.SepalWidth), data.Max(i => i.SepalWidth));
}
var network = new Network(new NeuralNet.TransferFunctions.HyperbolicTangentFunction(), true);
network.FillNetwork(4, 3, 6);
var train = data.Take(TrainSetSize).ToArray();
var inputAndExpectedResuls = train.Select(entry => new InputExpectedResult(entry.AsInput, entry.AsOutput));
var validation = data.Skip(TrainSetSize).ToArray();
var bp = new Backpropagate(network, 0.5, 3);
var trainData = inputAndExpectedResuls.ToArray();
int trains = 0;
double score = 0;
var start = Environment.TickCount;
while(score < 90) {
trains++;
bp.Train(trainData.OrderBy(i => random.Next()).ToArray());
var stats = NetworkValidation.Validate(network, inputAndExpectedResuls, IrisEntry.IsOutputSuccess);
score = stats.SuccessPercentage;
Console.WriteLine($"{trains,-4}" + stats.ToString());
}
Console.WriteLine($"\nTime elapsed: {Environment.TickCount - start}Ms");
Console.WriteLine("Done training");
var trainStats = NetworkValidation.Validate(network, train.Select(entry => new InputExpectedResult(entry.AsInput, entry.AsOutput)), IrisEntry.IsOutputSuccess);
var validateStats = NetworkValidation.Validate(network, validation.Select(entry => new InputExpectedResult(entry.AsInput, entry.AsOutput)), IrisEntry.IsOutputSuccess);
Console.WriteLine($"{trainStats.ToString()} TRAIN");
Console.WriteLine($"{validateStats.ToString()} VALIDATE");
Console.WriteLine($"{(trainStats + validateStats).ToString()} TOTAL");
Console.ReadKey();
}
public void TestTraining()
{
var sigmoid = new SigmoidFunction();
var net = new Network(sigmoid, true);
net.FillNetwork(2, 2, 2);
net.Nodes[0][0].GetOutgoingConnections()[0].Weight = .15;
net.Nodes[0][0].GetOutgoingConnections()[1].Weight = .2;
net.Nodes[0][1].GetOutgoingConnections()[0].Weight = .25;
net.Nodes[0][1].GetOutgoingConnections()[1].Weight = .3;
net.Nodes[1][0].GetOutgoingConnections()[0].Weight = .4;
net.Nodes[1][0].GetOutgoingConnections()[1].Weight = .45;
net.Nodes[1][1].GetOutgoingConnections()[0].Weight = .5;
net.Nodes[1][1].GetOutgoingConnections()[1].Weight = .55;
var expected = new InputExpectedResult(new double[] { .05, .1 }, new double[] { .01, .99 });
var before = NetworkValidation.Validate(net, new InputExpectedResult[] { expected }, (a, b) => true);
var bp = new Backpropagate(net, 0.5);
bp.Train(new InputExpectedResult[] { expected });
var after = NetworkValidation.Validate(net, new InputExpectedResult[] { expected }, (a, b) => true);
Assert.IsTrue(before.AvgSSE > after.AvgSSE);
}
private void InitNetwork(double learnRate, int microBatchsize, int inputHeight, int outputHeight, int[] hiddenHeights)
{
Network = new Network(TransferFunc, true);
Network.FillNetwork(inputHeight, outputHeight, hiddenHeights);
BackpropTrain = new Backpropagate(Network, learnRate, microBatchsize);
}