public void TestBias()
{
double input = 0.3;
double inpToOutWeight = 0.4;
double biasToOutWeight = 0.5;
var sigmoid = new SigmoidFunction();
var nw = new Network(sigmoid, false);
var inputNode = new Input(input);
var bias = new Bias();
var outputNode = new Perceptron(sigmoid, "Output");
Connection.Create(inpToOutWeight, inputNode, outputNode);
Connection.Create(biasToOutWeight, bias, outputNode);
nw.Nodes = new Node[][] {
new Node[] { inputNode },
new Node[] { outputNode }
};
var nwOut = nw.Nodes[1][0].Output;
//Output
// = Sig(inpToOut.Output + biasToOut.Output)
// = Sig((inpToOut.Weight * inpToOut.GetInput()) + (biasToOutWeight * 1))
// = Sig((inpToOut.Weight * inputNode.Output) + (biasToOutWeight))
// = Sig((this.weight * this.input) + (biasToOutWeight))
var expOut = sigmoid.Calculate((inpToOutWeight * input) + biasToOutWeight);
Assert.AreEqual(nwOut, expOut);
}
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);
}
public void TestSigmoid()
{
SigmoidFunction sf = new SigmoidFunction();
Assert.IsTrue(sf.Calculate(10, 20) > 0.9);
Assert.IsTrue(sf.Calculate(-10, -20) < 0.1);
Assert.IsTrue(sf.Calculate(-1, 1) > 0.4 && sf.Calculate(-1, 1) < 0.6);
}
public void TestNetworkOutput()
{
var rand = new Random();
double input = (double)(rand.NextDouble());
double weight = (double)(rand.NextDouble());
var sigmoid = new SigmoidFunction();
var nw = new Network(sigmoid, false);
var inputNode = new Input(input);
var outputNode = new Perceptron(sigmoid, "Output");
Connection.Create(weight, inputNode, outputNode);
nw.Nodes = new Node[][] {
new Node[] { inputNode },
new Node[] { outputNode }
};
var nwOut = nw.CurOutput()[0];
Assert.AreEqual(outputNode.Output, nwOut);
var expOut = sigmoid.Calculate(input * weight);
Assert.AreEqual(nwOut, expOut);
}
public void TestGetInputResult()
{
var rand = new Random();
double input = (double)(rand.NextDouble() * 100 + 1);
double weight = (double)(rand.NextDouble() * 2);
var sigmoid = new SigmoidFunction();
var nw = new Network(sigmoid, false);
var inputNode = new Input(input);
var outputNode = new Perceptron(sigmoid, "Output");
Connection.Create(weight, inputNode, outputNode);
nw.Nodes = new Node[][] {
new Node[] { inputNode },
new Node[] { outputNode }
};
var nwOut = nw.GetInputResult(input)[0];
//Output
// = Sig(inpToOut.Output)
// = Sig(inpToOut.Weight * inpToOut.GetInput())
// = Sig(inpToOut.Weight * inputNode.Output)
// = Sig(this.weight * this.input)
var expOut = sigmoid.Calculate(weight * input);
Assert.AreEqual(nwOut, expOut);
}
public void Init(int imgDim, double learnRate, int microBatchsize, int loadBatchsize, string imgFolder, TransferFunctionType funcType, int inputHeight, int outputHeight, int[] hiddenHeights)
{
switch(funcType) {
case TransferFunctionType.Sigmoid:
TransferFunc = new SigmoidFunction();
break;
case TransferFunctionType.HyperbolicTangent:
TransferFunc = new HyperbolicTangentFunction();
break;
}
ImageDimensions = imgDim;
ImgLoader = new LazyTrainImgLoader(imgFolder, TransferFunc, true, true, imgDim, loadBatchsize/*, TODO: Support non-digits*/);
PropertyChanged?.Invoke(this, new PropertyChangedEventArgs("ImgCount"));
InitNetwork(learnRate, microBatchsize, inputHeight, outputHeight, hiddenHeights);
}
