public void ShouldTrainUsingCrossEntropyCostFunction()
{
var stochasticGradientDescent = new StochasticGradientDescent(new CrossEntropy(), _layers, 3000, 4, 5D, 0);
stochasticGradientDescent.Train(_trainingData);
var neuralNetwork = new NeuralNetwork(_layers, _layers.First().PrimaryNeuronsCount);
var result1 = neuralNetwork.Run(_trainingData[0].Inputs);
result1.Should().HaveCount(1);
Math.Round(result1[0], 10).Should().Be(0.0005468953D);
var result2 = neuralNetwork.Run(_trainingData[1].Inputs);
result2.Should().HaveCount(1);
Math.Round(result2[0], 10).Should().Be(0.9993728892D);
var result3 = neuralNetwork.Run(_trainingData[2].Inputs);
result3.Should().HaveCount(1);
Math.Round(result3[0], 10).Should().Be(0.9994636693D);
var result4 = neuralNetwork.Run(_trainingData[3].Inputs);
result4.Should().HaveCount(1);
Math.Round(result4[0], 10).Should().Be(0.0008765251D);
}
public void ShouldTrainUsingQuadraticCostFunction()
{
var stochasticGradientDescent = new StochasticGradientDescent(new Quadratic(), _layers, 3000, 4, 5D, 0);
stochasticGradientDescent.Train(_trainingData);
var neuralNetwork = new NeuralNetwork(_layers, _layers.First().PrimaryNeuronsCount);
var result1 = neuralNetwork.Run(_trainingData[0].Inputs);
result1.Should().HaveCount(1);
Math.Round(result1[0], 10).Should().Be(0.0245579310D);
var result2 = neuralNetwork.Run(_trainingData[1].Inputs);
result2.Should().HaveCount(1);
Math.Round(result2[0], 10).Should().Be(0.9661695582D);
var result3 = neuralNetwork.Run(_trainingData[2].Inputs);
result3.Should().HaveCount(1);
Math.Round(result3[0], 10).Should().Be(0.9852113647D);
var result4 = neuralNetwork.Run(_trainingData[3].Inputs);
result4.Should().HaveCount(1);
Math.Round(result4[0], 10).Should().Be(0.0320611480D);
}
public void ShouldTrainUsingRegularizationParam()
{
var stochasticGradientDescent = new StochasticGradientDescent(new CrossEntropy(), _layers, 3000, 4, 5D, 0.01D);
stochasticGradientDescent.Train(_trainingData);
var neuralNetwork = new NeuralNetwork(_layers, _layers.First().PrimaryNeuronsCount);
var result1 = neuralNetwork.Run(_trainingData[0].Inputs);
result1.Should().HaveCount(1);
Math.Round(result1[0], 10).Should().Be(0.0285179059D);
var result2 = neuralNetwork.Run(_trainingData[1].Inputs);
result2.Should().HaveCount(1);
Math.Round(result2[0], 10).Should().Be(0.9714820792D);
var result3 = neuralNetwork.Run(_trainingData[2].Inputs);
result3.Should().HaveCount(1);
Math.Round(result3[0], 10).Should().Be(0.9714820797D);
var result4 = neuralNetwork.Run(_trainingData[3].Inputs);
result4.Should().HaveCount(1);
Math.Round(result4[0], 10).Should().Be(0.0285163624D);
}
public void Run()
{
var layer = new ILayer[] { new FullyConnectedLayer(new Sigmoid(), 3, 2), new FullyConnectedLayer(new Sigmoid(), 1, 3) };
Randomiser.Randomise(layer, new Random(5));
Console.WriteLine("Evaluationg untrained neural network");
DisplayEvaluation(layer);
var stochasticGradientDescent = new StochasticGradientDescent(new CrossEntropy(), layer, 3000, 4, 5D, 0D);
var trainingData = new List <TrainingElement>
{
new TrainingElement
{
Inputs = new double[] { 0D, 0D },
ExpectedOutputs = new double[] { 0D }
},
new TrainingElement
{
Inputs = new double[] { 1D, 0D },
ExpectedOutputs = new double[] { 1D }
},
new TrainingElement
{
Inputs = new double[] { 0D, 1D },
ExpectedOutputs = new double[] { 1D }
},
new TrainingElement
{
Inputs = new double[] { 1D, 1d },
ExpectedOutputs = new double[] { 0D }
}
};
stochasticGradientDescent.Train(trainingData);
Console.WriteLine("Evaluationg trained neural network");
DisplayEvaluation(layer);
}
public void Fit(Matrix <double> x, Vector <double> y, bool addOnes = true, bool verbose = false) => _sgd.Train(x, y, addOnes, true, verbose);