| 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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