static void Main(string[] args)
{
// 階層型ニューラルネットワークを構成
var nn = new HierarchicalNetwork(2, 2, 1);
nn[1, 0].Weights[0] = 10.0;
nn[1, 0].Weights[1] = 10.0;
nn[1, 0].Threshold = -5.0;
nn[1, 1].Weights[0] = 10.0;
nn[1, 1].Weights[1] = 10.0;
nn[1, 1].Threshold = -15.0;
nn[2, 0].Weights[0] = 10.0;
nn[2, 0].Weights[1] = -10.0;
nn[2, 0].Threshold = -5.0;
// 発火
nn.SetInputs(new ConstOutput(0.0), new ConstOutput(1.0));
nn.Fire();
// グラフを出力
var gen = new GraphGenerator();
using (var writer = new StreamWriter("graph.gv"))
{
gen.Generate(writer, nn);
}
}
static void Main(string[] args)
{
Func<double, double> f = ((double x) => 1 / (1 + Math.Exp(-x)));
Func<double, double> df = (x => f(x) * (1 - f(x)));
// ネットワークの情報読み込み
HierarchicalNetwork nn;
var examples = new List<Tuple<double[], double[]>>();
Console.Error.Write("path >");
var path = Console.ReadLine();
using (var reader = new StreamReader(path))
{
nn = new HierarchicalNetwork(reader.ReadLine().Split(' ').Select(v => int.Parse(v)).ToArray());
var exNum = int.Parse(reader.ReadLine());
for (var i = 0; i < exNum; ++i)
{
var io = reader.ReadLine().Split('|').ToList();
examples.Add(Tuple.Create(
io[0].Split(' ').Select(v => double.Parse(v)).ToArray(),
io[1].Split(' ').Select(v => double.Parse(v)).ToArray()
));
}
}
// 学習
var bp = new Backpropagation(nn, df, 0.8, 0.75);
var rnd = new Random();
using (var writer = new StreamWriter("error.csv"))
{
for (var i = 0; i < 1000; ++i)
{
var ex = examples.OrderBy(v => Guid.NewGuid()).ToArray(); // shuffle
var errSum = 0.0;
for (var j = 0; j < ex.Length; ++j)
{
bp.Train(ex[j].Item1, ex[j].Item2);
errSum += bp.Error;
}
writer.WriteLine("{0},{1}", i, errSum / ex.Length);
}
}
while (true)
{
Console.Error.Write("input >");
var inputs = Console.ReadLine().Split(' ').Select(v => new ConstOutput(double.Parse(v))).ToArray();
nn.SetInputs(inputs);
nn.Fire();
foreach (var o in nn.GetOutputs())
{
Console.WriteLine("{0:0.0########################################################}", o);
}
}
}
