public void Play()
{
Console.WriteLine("XOR Learn!");
var model = new NeuralNetwork()
.AddFullLayer(2)
.AddSigmoid()
.AddFullLayer(2)
.AddSigmoid()
.AddFullLayer(1)
.AddSigmoid()
.UseCrossEntropyLoss()
.UseGradientDescent(0.01)
.UseRidge(0.001);
var machine = new ClassificationMachine(model);
var(trainX, trainY) = GetXorData(1000);
var(testX, testY) = GetXorData(500);
var tain = new Trainer(model, 32, 200);
tain.StartTrain(trainX, trainY, testX, testY);
Console.WriteLine("Complete!");
while (true)
{
var str1 = Console.ReadLine();
var str2 = Console.ReadLine();
if (str1 == "" || str2 == "")
{
return;
}
var a1 = double.Parse(str1);
var a2 = double.Parse(str2);
var output = machine.PredictValue(new TensorOld(new double[] { a1, a2 }, 1, 2));
var result = output[0];
var p = machine.LastRawResult.GetValue();
Console.WriteLine($"result is {result} with probability:{result * p + (1 - result) * (1 - p)}");
}
}
public void Play()
{
var iris = AyxCsvReader.Instance.ReadCsvFileDataTable("Data/iris.csv");
var labels = DataFormat.DataTableToList(iris, "Species").ToList();
var codec = new OneHotCodec(labels);
var y = codec.Encode(labels);
var X = DataFormat.DataTableToTensor(iris, 1, 2, 3, 4);
var model = new NeuralNetwork()
.AddFullLayer(5)
.AddReLU()
.AddFullLayer(5)
.AddReLU()
.AddFullLayer(codec.Length)
.AddSoftmax()
.UseCrossEntropyLoss()
.UseGradientDescent(0.001);
var trainer = new Trainer(model, 16, 200, true);
trainer.StartTrain(X, y, null, null);
var machine = new ClassificationMachine(model);
machine.LabelCodec = codec;
while (true)
{
Console.Write("input Sepal.Length:");
var a1 = double.Parse(Console.ReadLine());
Console.Write("input Sepal.Width:");
var a2 = double.Parse(Console.ReadLine());
Console.Write("input Petal.Length:");
var a3 = double.Parse(Console.ReadLine());
Console.Write("input Petal.Width:");
var a4 = double.Parse(Console.ReadLine());
var input = new TensorOld(new double[] { a1, a2, a3, a4 }, 1, 4);
Console.WriteLine($"you input is {machine.Predict(input).First()}, Codec:{machine.LastCodecResult}, Raw:{machine.LastRawResult}");
}
}
private void UseButton_Click(object sender, RoutedEventArgs e)
{
var info = TrainerGrid.SelectedItem as TrainerInfo;
if (info == null)
{
MessageBox.Show("select a Machine first!");
return;
}
foreach (TrainerInfo item in TrainerGrid.Items)
{
item.IsUsing = false;
}
info.IsUsing = true;
var trainer = Storage.Load <Trainer>(info.FileName);
machine = trainer.GetClassificationMachine();
}
