// create a multi-layer perceptron with 2 input neurons, 3 hidden neurons, and 1 output neuron var network = new ActivationNetwork(new SigmoidFunction(), 2, 3, 1); // define the training data double[][] input = { new[] { 0.1, 0.2 }, new[] { 0.3, 0.4 }, new[] { 0.5, 0.6 } }; double[][] output = { new[] { 0.3 }, new[] { 0.4 }, new[] { 0.5 } }; // train the network using backpropagation algorithm var teacher = new BackPropagationLearning(network); teacher.RunEpoch(input, output); // test the network with new data var result = network.Compute(new[] { 0.7, 0.8 });
// create a multiclass classification pipeline var pipeline = new MulticlassClassificationPipeline(new TextLoader(dataPath).CreateFrom(), new ColumnConcatenator("Features", "Feature1", "Feature2"), new MultilayerPerceptronClassifier { Layers = new[] { new Layer { InputDim = 2, OutputDim = 3, Activation = new ReLuActivation() }, new Layer { InputDim = 3, OutputDim = 4, Activation = new SoftmaxActivation() } }, Epoch = 100, LearningRate = 0.1f }); // train the model var model = pipeline.Train (); // test the model with new data var prediction = model.Predict(new InputData { Feature1 = 0.7f, Feature2 = 0.8f });
// create a multi-layer perceptron with 2 input neurons, 3 hidden neurons, and 1 output neuron var network = new ActivationNetwork(new SigmoidFunction(), 2, 3, 1); // create the teacher with backpropagation algorithm var teacher = new BackPropagationLearning(network); // train the network using backpropagation algorithm double[][] input = { new[] { 0.1, 0.2 }, new[] { 0.3, 0.4 }, new[] { 0.5, 0.6 } }; double[][] output = { new[] { 0.3 }, new[] { 0.4 }, new[] { 0.5 } }; for (var i = 0; i < 100; i++) { teacher.RunEpoch(input, output); } // test the network with new data var result = network.Compute(new[] { 0.7, 0.8 });In summary, there are several C# packages available for multi-layer perceptron implementation, including Accord.NET, ML.NET, and MathNet.Numerics. These examples show how to create and train a multi-layer perceptron with different activation functions and optimization algorithms.