c#神经网络演示代码

using System;class SimpleNeuralNetwork
{// 定义学习率和网络参数static double learningRate = 0.1;static int inputLayerSize = 2;static int hiddenLayerSize = 3;static int outputLayerSize = 1;// 权重矩阵static double[,] weightsInputHidden;static double[,] weightsHiddenOutput;// 偏置static double[] biasHidden;static double[] biasOutput;static void Main(string[] args){// 初始化权重和偏置weightsInputHidden = InitializeWeights(inputLayerSize, hiddenLayerSize);weightsHiddenOutput = InitializeWeights(hiddenLayerSize, outputLayerSize);biasHidden = InitializeBias(hiddenLayerSize);biasOutput = InitializeBias(outputLayerSize);// 训练集 (输入数据和目标输出)double[][] trainingInputs = {new double[] { 0.1, 0.5 },new double[] { 0.9, 0.8 },new double[] { 0.4, 0.2 }};double[][] trainingTargets = {new double[] { 0.05 },new double[] { 0.72 },new double[] { 0.08 }};// 训练网络for (int epoch = 0; epoch < 1000000; epoch++){for (int i = 0; i < trainingInputs.Length; i++){Train(trainingInputs[i], trainingTargets[i]);}}// 测试网络Console.WriteLine("训练完成，开始测试：");foreach (var input in trainingInputs){double[] output = Forward(input);Console.WriteLine($"输入: [{input[0]}, {input[1]}] -> 预测输出: {output[0]:F4}");}}// 初始化权重矩阵static double[,] InitializeWeights(int rows, int cols){var weights = new double[rows, cols];Random rand = new Random();for (int i = 0; i < rows; i++){for (int j = 0; j < cols; j++){weights[i, j] = rand.NextDouble() - 0.5; // 随机值在[-0.5, 0.5]}}return weights;}// 初始化偏置static double[] InitializeBias(int size){var bias = new double[size];Random rand = new Random();for (int i = 0; i < size; i++){bias[i] = rand.NextDouble() - 0.5; // 随机值在[-0.5, 0.5]}return bias;}// 激活函数 (Sigmoid)static double Sigmoid(double x){return 1 / (1 + Math.Exp(-x));}// 激活函数的导数static double SigmoidDerivative(double x){return x * (1 - x);}// 前向传播static double[] Forward(double[] inputs){// 输入层到隐藏层double[] hidden = new double[hiddenLayerSize];for (int i = 0; i < hiddenLayerSize; i++){double sum = biasHidden[i];for (int j = 0; j < inputLayerSize; j++){sum += inputs[j] * weightsInputHidden[j, i];}hidden[i] = Sigmoid(sum);}// 隐藏层到输出层double[] output = new double[outputLayerSize];for (int i = 0; i < outputLayerSize; i++){double sum = biasOutput[i];for (int j = 0; j < hiddenLayerSize; j++){sum += hidden[j] * weightsHiddenOutput[j, i];}output[i] = Sigmoid(sum);}return output;}// 反向传播static void Backpropagation(double[] inputs, double[] targets, double[] output){// 计算输出层误差double[] outputErrors = new double[outputLayerSize];for (int i = 0; i < outputLayerSize; i++){outputErrors[i] = (targets[i] - output[i]) * SigmoidDerivative(output[i]);}// 计算隐藏层误差double[] hidden = new double[hiddenLayerSize];for (int i = 0; i < hiddenLayerSize; i++){double sum = biasHidden[i];for (int j = 0; j < inputLayerSize; j++){sum += inputs[j] * weightsInputHidden[j, i];}hidden[i] = Sigmoid(sum);}double[] hiddenErrors = new double[hiddenLayerSize];for (int i = 0; i < hiddenLayerSize; i++){double error = 0;for (int j = 0; j < outputLayerSize; j++){error += outputErrors[j] * weightsHiddenOutput[i, j];}hiddenErrors[i] = error * SigmoidDerivative(hidden[i]);}// 更新隐藏层到输出层的权重和偏置for (int i = 0; i < hiddenLayerSize; i++){for (int j = 0; j < outputLayerSize; j++){weightsHiddenOutput[i, j] += learningRate * outputErrors[j] * hidden[i];}}for (int i = 0; i < outputLayerSize; i++){biasOutput[i] += learningRate * outputErrors[i];}// 更新输入层到隐藏层的权重和偏置for (int i = 0; i < inputLayerSize; i++){for (int j = 0; j < hiddenLayerSize; j++){weightsInputHidden[i, j] += learningRate * hiddenErrors[j] * inputs[i];}}for (int i = 0; i < hiddenLayerSize; i++){biasHidden[i] += learningRate * hiddenErrors[i];}}// 训练函数static void Train(double[] inputs, double[] targets){// 前向传播double[] output = Forward(inputs);// 反向传播Backpropagation(inputs, targets, output);}
}