var network = new SimpleNeuralNetwork(3); | |
var layerFactory = new NeuralLayerFactory(); | |
network.AddLayer(layerFactory.CreateNeuralLayer(3, new RectifiedActivationFuncion(), new WeightedSumFunction())); | |
network.AddLayer(layerFactory.CreateNeuralLayer(1, new SigmoidActivationFunction(0.7), new WeightedSumFunction())); | |
network.PushExpectedValues( | |
new double[][] { | |
new double[] { 0 }, | |
new double[] { 1 }, | |
new double[] { 1 }, | |
new double[] { 0 }, | |
new double[] { 1 }, | |
new double[] { 0 }, | |
new double[] { 0 }, | |
}); | |
network.Train( | |
new double[][] { | |
new double[] { 150, 2, 0 }, | |
new double[] { 1002, 56, 1 }, | |
new double[] { 1060, 59, 1 }, | |
new double[] { 200, 3, 0 }, | |
new double[] { 300, 3, 1 }, | |
new double[] { 120, 1, 0 }, | |
new double[] { 80, 1, 0 }, | |
}, 10000); | |
network.PushInputValues(new double[] { 1054, 54, 1 }); | |
var outputs = network.GetOutput(); |
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