//识别
private void Button_Click_1(object sender, RoutedEventArgs e)
{
if (machine == null)
{
MessageBox.Show("please select a machine first!");
return;
}
RenderTargetBitmap bmp = new RenderTargetBitmap((int)WritingBoard.ActualWidth, (int)WritingBoard.ActualHeight, 0, 0, PixelFormats.Default);
bmp.Render(WritingBoard);
//
var con = new FormatConvertedBitmap();
con.BeginInit();
con.Source = bmp;
con.DestinationFormat = PixelFormats.Gray8;
con.EndInit();
var trans = new TransformedBitmap();
trans.BeginInit();
trans.Source = con;
trans.Transform = new ScaleTransform(0.1, 0.1);
trans.EndInit();
var buff = BitmapSourceToArray(trans);
var data = new double[buff.Length];
for (int i = 0; i < buff.Length; i++)
{
data[i] = 255 - buff[i];
}
var tensor = new TensorOld(data, 1, buff.Length);
var predict = machine.Predict(tensor);
PredictText.Text = predict[0];
var predictProb = machine.LastRawResult.Max();
ProbText.Text = $"Probability:{predictProb.ToString("F8")}";
ShowProbability();
}
public void Play()
{
var iris = AyxCsvReader.Instance.ReadCsvFileDataTable("Data/iris.csv");
var labels = DataFormat.DataTableToList(iris, "Species").ToList();
var codec = new OneHotCodec(labels);
var y = codec.Encode(labels);
var X = DataFormat.DataTableToTensor(iris, 1, 2, 3, 4);
var model = new NeuralNetwork()
.AddFullLayer(5)
.AddReLU()
.AddFullLayer(5)
.AddReLU()
.AddFullLayer(codec.Length)
.AddSoftmax()
.UseCrossEntropyLoss()
.UseGradientDescent(0.001);
var trainer = new Trainer(model, 16, 200, true);
trainer.StartTrain(X, y, null, null);
var machine = new ClassificationMachine(model);
machine.LabelCodec = codec;
while (true)
{
Console.Write("input Sepal.Length:");
var a1 = double.Parse(Console.ReadLine());
Console.Write("input Sepal.Width:");
var a2 = double.Parse(Console.ReadLine());
Console.Write("input Petal.Length:");
var a3 = double.Parse(Console.ReadLine());
Console.Write("input Petal.Width:");
var a4 = double.Parse(Console.ReadLine());
var input = new TensorOld(new double[] { a1, a2, a3, a4 }, 1, 4);
Console.WriteLine($"you input is {machine.Predict(input).First()}, Codec:{machine.LastCodecResult}, Raw:{machine.LastRawResult}");
}
}