static void Main(string[] args)
{
//不使用預設的激發函數組，預設激發函數為LogisticSigmoid
Perceptron.Default_Activation = ActivationFunction.HyperbolicTangent;
Perceptron.Default_DiffActivation = ActivationFunction.Diff_HyperbolicTangent;
//建構一個輸入層數值數量為2 感知器權重亂數範圍為-1~1
//隱藏層節點數量為4 輸出層數量為1的神經網路
PerceptronNetwork network = new PerceptronNetwork(2, -1, 1, new int[] { 4,1 });
LearnData[] data = new LearnData[] {//學習用資料
new LearnData() { Input = new double[] {0,0 },Output = new double[] { 0 }},
new LearnData() { Input = new double[] {1,1 },Output = new double[] { 0 }},
new LearnData() { Input = new double[] {1,0 },Output = new double[] { 1 }},
new LearnData() { Input = new double[] {0,1 },Output = new double[] { 1 }},
};
LearnData.Standardize(data, -1, 1, -1, 1);//將資料轉換為符合Tanh激發函數的資料
//呼叫學習函數，速率0.8，慣性0.6，目標誤差值0.05
network.Train(data,0.8,0.6, 0.05);
//測試
foreach (var item in data) {
Console.WriteLine($"測試({string.Join(",", item.Input)}) => {string.Join(",", network.Compute(item.Input))}");
}
//儲存神經網路結果
StreamWriter writer = new StreamWriter("output.json");
writer.Write(network.ToJObject());//匯出JSON，也可使用Load方法反序列化
writer.Close();
Console.ReadKey();
}
static void Main(string[] args)
{
//不使用預設的激發函數組，預設激發函數為LogisticSigmoid
Perceptron.Default_Activation = ActivationFunction.HyperbolicTangent;
Perceptron.Default_DiffActivation = ActivationFunction.Diff_HyperbolicTangent;
//建構一個輸入層數值數量為2 感知器權重亂數範圍為-1~1
//隱藏層節點數量為4 輸出層數量為1的神經網路
PerceptronNetwork network = new PerceptronNetwork(2, -1, 1, new int[] { 4, 1 });
LearnData[] data = new LearnData[] {//學習用資料
new LearnData()
{
Input = new double[] { 0, 0 }, Output = new double[] { 0 }
},
new LearnData()
{
Input = new double[] { 1, 1 }, Output = new double[] { 0 }
},
new LearnData()
{
Input = new double[] { 1, 0 }, Output = new double[] { 1 }
},
new LearnData()
{
Input = new double[] { 0, 1 }, Output = new double[] { 1 }
},
};
LearnData.Standardize(data, -1, 1, -1, 1);//將資料轉換為符合Tanh激發函數的資料
//呼叫學習函數，速率0.8，慣性0.6，目標誤差值0.05
network.Train(data, 0.8, 0.6, 0.05);
//測試
foreach (var item in data)
{
Console.WriteLine($"測試({string.Join(",", item.Input)}) => {string.Join(",", network.Compute(item.Input))}");
}
//儲存神經網路結果
StreamWriter writer = new StreamWriter("output.json");
writer.Write(network.ToJObject());//匯出JSON，也可使用Load方法反序列化
writer.Close();
Console.ReadKey();
}