cscの作法 その32 ニューラルネットワーク

3277 ワード
.NETFramework2.0 C# ニューラルネットワーク TensorFlow.js C# テキストリンク

概要

cscの作法、調べてみた。
tensorflow.jsでxorを学習して、バイアスとウェイトを取り出して、c#で推論してみた。

写真

サンプルコード

using System;
using System.Windows.Forms;
using System.Drawing;

class form1: Form {
    form1() {
        Text = "nn";
        ClientSize = new Size(200, 200);
        double[][] w1 = {
            new double[] {-0.8971094, 1.4029436, -1.3633214, -0.775598, 1.2531579, 1.0529767, 1.3460118, -1.5597397},
            new double[] {1.57735, -1.1888924, 1.6184694, -0.5399966, -0.9783241, 0.9480417, 0.9573314, -1.1738404}
        };
        double[] b1 = {0.428151, 0.7863605, 0.8015493, 0.8869765, 0.6552361, 0.3099377, 0.1825127, 0.0495548};
        double[][] w2 = {
            new double[] {-1.7588012},
            new double[] {-1.5193572},
            new double[] {-1.1239196},
            new double[] {0.8726832},
            new double[] {-1.7272931},
            new double[] {1.4907871},
            new double[] {1.1091278},
            new double[] {-0.9800876}
        };
        double[] b2 = {0.1271933};
        string res = "";
        for (int i = 0; i < 4; i++)
        {
            int k;
            int l;
            double[] x = {0, 0};
            double[] y = {0};
            double[] h = {0, 0, 0, 0, 0, 0, 0, 0};
            if (i == 0)
            {
                x[0] = 1.0;
                x[1] = 0;
                res += "1, 0 = ";
            }
            if (i == 1)
            {
                x[0] = 0;
                x[1] = 1.0;
                res += "0, 1 = ";
            }
            if (i == 2)
            {
                x[0] = 1.0;
                x[1] = 1.0;
                res += "1, 1 = ";
            }
            if (i == 3)
            {
                x[0] = 0;
                x[1] = 0;
                res += "0, 0 = ";
            }
            for (k = 0; k < 2; k++)
            {
                for (l = 0; l < 8; l++)
                {
                    h[l] += x[k] * w1[k][l];
                }
            }
            for (l = 0; l < 8; l++)
            {
                h[l] += b1[l];
                h[l] = tanh(h[l]);
            }
            for (k = 0; k < 8; k++)
            {
                for (l = 0; l < 1; l++)
                {
                    y[l] += h[k] * w2[k][l];
                }
            }
            for (k = 0; k < 1; k++)
            {
                y[k] += b2[k];
                y[k] = sigmoid(y[k]);
            }
            res += y[0].ToString() + "\r\n";
        }
        MessageBox.Show(res);
    }
    double tanh(double x) {
        double a = Math.Exp(x);
        double b = Math.Exp(-x);
        return (a - b) / (a + b);
    }
    double sigmoid(double x) {
        return 1.0 / (1.0 + Math.Exp(-x));
    }
    [STAThread]
    public static void Main() {
        Application.Run(new form1());
    }
}

以上。