C# - 3층 구조의 신경망(분류)

지난 글에 이어서,

C# - 3층 구조의 신경망
; https://www.sysnet.pe.kr/2/0/11969

실체가 손에 잡히는 딥러닝
; http://www.yes24.com/Product/Goods/74258238

# http://nanya-kanya.net/index.php/1232/#outline__1_30

import numpy as np
import matplotlib.pyplot as plt

X = np.arange(-1.0, 1.0, 0.1)
Y = np.arange(-1.0, 1.0, 0.1)

w_im = np.array([[1.0, 2.0],
                [2.0, 3.0]])

w_mo = np.array([[ -1.0, 1.0],
                [1.0, -1.0]])

b_im = np.array([0.3, -0.3])
b_mo = np.array([0.4, 0.1])

def middle_layer(x, w, b):
    u = np.dot(x, w) + b
    return 1 / (1 + np.exp(-u))

def output_layer(x, w, b):
    u = np.dot(x, w) + b
    return np.exp(u) / np.sum(np.exp(u))

x_1 = []
y_1 = []
x_2 = []
y_2 = []

for i in range(20):
    for j in range(20):
        inp = np.array([X[i], Y[j]])
        mid = middle_layer(inp, w_im, b_im)
        out = output_layer(mid, w_mo, b_mo)

        if out[0] > out[1]:
            x_1.append(X[i])
            y_1.append(Y[j])
        else:
            x_2.append(X[i])
            y_2.append(Y[j])

        
plt.scatter(x_1, y_1, marker="+")
plt.scatter(x_2, y_2, marker="o")
plt.show()

using MathNet.Numerics.LinearAlgebra;
using System;
using System.Linq;

using np = PythonUtils;
using vector = MathNet.Numerics.LinearAlgebra.Vector<double>;
using matrix = MathNet.Numerics.LinearAlgebra.Matrix<double>;
using System.Collections.Generic;

class Program
{
    static void Main(string[] args)
    {
        var X = np.arange(-1.0, 1.0, 0.1).ToArray();
        var Y = np.arange(-1.0, 1.0, 0.1).ToArray();

        List<double> x1 = new List<double>();
        List<double> x2 = new List<double>();
        List<double> y1 = new List<double>();
        List<double> y2 = new List<double>();

        matrix w_im = GetMatrix(new[] { 1.0, 2.0 }, new[] { 2.0, 3.0 });
        matrix w_mo = GetMatrix(new[] { -1.0, 1.0 }, new[] { 1.0, -1.0 });

        vector b_im = GetVector(0.3, -0.3);
        vector b_mo = GetVector(0.4, 0.1);

        Func<vector, matrix, vector, vector> middle_layer = (x, w, b) =>
        {
            vector u = x * w + b;
            return 1 / (1 + np.exp(-u)); // sigmoid
        };

        Func<vector, matrix, vector, vector> output_layer = (x, w, b) =>
        {
            vector u = x * w + b;
            return np.exp(u) / np.sum(u); // softmax
        };

        for (int i = 0; i < X.Length; i++)
        {
            for (int j = 0; j < Y.Length; j++)
            {
                var inp = GetVector(X[i], Y[j]);
                var mid = middle_layer(inp, w_im, b_im);
                var outp = output_layer(mid, w_mo, b_mo);

                if (outp[0] > outp[1])
                {
                    x1.Add(X[i]);
                    y1.Add(Y[j]);
                }
                else
                {
                    x2.Add(X[i]);
                    y2.Add(Y[j]);
                }
            }
        }

        OutputImage("layer3_neuron_classification.png");

        void OutputImage(string fileName)
        {
            Gridmap grid = new Gridmap(471, 471);
            grid.Show(x1.ToArray(), y1.ToArray(), x2.ToArray(), y2.ToArray(), fileName);
        }
    }
}