.NET中的神经网络示例

using System.Numerics;
using System.Threading.Tasks;
using Microsoft.ML.Runtime.Data;

// 定义神经网络结构
public class NeuralNet
{
    private const int numberOfInputDimensions = 2; // 输入的维度
    private const int numberOfHiddenDimensions = 3; // 隐藏层的维度
    private const int numberOfOutputDimensions = 5; // 输出的维度

    // 定义神经网络层
    public NeuralLayer[] layers { get; set; }

    public NeuralNet(params (int, int)[] layerSizes)
    {
        // 初始化层的个数和维度
        int totalLayerSize = numberOfInputDimensions + numberOfHiddenDimensions + numberOfOutputDimensions;
        layers = new NeuralLayer[totalLayerSize];
        // 添加输入层
        layers[0] = new NeuralLayer(numberOfInputDimensions);
        // 添加隐藏层
        for (int i = 0; i< numberOfHiddenDimensions; ++i)
        {
            NeuralLayer tempLayer = new NeuralLayer(numberOfHiddenDimensions);
            layers[i + 1] = tempLayer;
        } // 添加输出层
        layers[totalLayerSize - 1] = new NeuralLayer(numberOfOutputDimensions);
    }

    public async Task Train(double[] inputArrays, double[] desiredOutput)
    {
        int epochs = 10000;
        foreach (double[] inputs in inputArrays)
        {
            // 模拟神经元行为
            int randomIndex = 0;
            var x = inputArrays[randomIndex++];
            var y = desiredOutput[randomIndex++];
            var bias = 0.0;
            var sigmoidFunction = function.Sigmoid(x, bias);
            // 计算误差
            var delta = y - sigmoidFunction;
            delta *= learningRate;
            var deltaSum = new Vector<double>(layers.Length);
            foreach (NeuralLayer layer in layers)
            {
                deltaSum.Add(Math.DenseMultiply(layer.WeightsBiasDotProduct(x, delta), delta) / layer.NumberOfConnections);
            }
            // 更新权重
            foreach (NeuralLayer layer in layers.SkipLast())
            {
                deltaSum.Add(Math.DenseMultiply(layer.WeightsBiasDotProduct(x, deltaSum.ElementAt(layers.Length - (i - 1))), deltaSum.ElementAt(layers.Length - (i - 2))));
            }
            await Task.Delay(100);
        }
    }

    public async Task PerformPrediction(double[] x)
    {
        int randomIndex = 0;
        var sigmoidInput = x[randomIndex++];
        var bias = 0.0;
        var sigmoidFunction = function.Sigmoid(sigmoidInput, bias);
        return sigmoidFunction;
    }

    // 定义类