public async Task <Inceptionv3_convertedOutput> Evaluate(StorageFile file)
{
Inceptionv3_convertedInput tensorInput = new Inceptionv3_convertedInput();
byte[] image = await ResizedImage(file, INPUT_WIDTH, INPUT_HEIGHT);
List <float> input = new List <float>();
List <float> R = new List <float>();
List <float> G = new List <float>();
List <float> B = new List <float>();
for (int j = 0; j < INPUT_HEIGHT; j++)
{
for (int i = 0; i < INPUT_WIDTH; i++)
{
R.Add(GetPixel(image, i, j, INPUT_WIDTH, INPUT_HEIGHT).R / 255f);
G.Add(GetPixel(image, i, j, INPUT_WIDTH, INPUT_HEIGHT).G / 255f);
B.Add(GetPixel(image, i, j, INPUT_WIDTH, INPUT_HEIGHT).B / 255f);
}
}
input.AddRange(R);
input.AddRange(G);
input.AddRange(B);
tensorInput.input_1_0 = TensorFloat.CreateFromArray(new long[] { 1, 256, 256, 3 }, input.ToArray());
return(await Model.EvaluateAsync(tensorInput));
}
private unsafe LearningModelBinding EvaluateContrastAndBrightnessSession(object input, object output)
{
var slope = Math.Tan(ContrastMaxSlider.Value * 3.14159 / 2);
var yintercept = -255 * (ContrastMinSlider.Value * 2 - 1);
if (yintercept < 0)
{
// it was the x-intercept
yintercept = slope * yintercept;
}
var binding = new LearningModelBinding(contrastEffectSession_);
binding.Bind("Input", input);
binding.Bind("Slope", TensorFloat.CreateFromArray(new long[] { 1 }, new float[] { (float)slope }));
binding.Bind("YIntercept", TensorFloat.CreateFromArray(new long[] { 1 }, new float[] { (float)yintercept }));
var outputBindProperties = new PropertySet();
outputBindProperties.Add("DisableTensorCpuSync", PropertyValue.CreateBoolean(true));
binding.Bind("Output", output, outputBindProperties);
EvaluateInternal(contrastEffectSession_, binding);
return(binding);
}
private async void RecogNumberFromInk()
{
// 从文件加载模型
var modelFile = await StorageFile.GetFileFromApplicationUriAsync(new Uri($"ms-appx:///Assets/mnist.onnx"));
var model = await mnistModel.CreateFromStreamAsync(modelFile);
// 组织输入
var inputArray = await GetInputDataFromInk();
var inputTensor = TensorFloat.CreateFromArray(new List <long> {
784
}, inputArray);
var modelInput = new mnistInput {
port = inputTensor
};
// 推理
var result = await model.EvaluateAsync(modelInput);
// 得到每个数字的得分
var scoreList = result.dense3port.GetAsVectorView().ToList();
// 从输出中取出得分最高的
var max = scoreList.IndexOf(scoreList.Max());
// 显示在控件中
lbResult.Text = max.ToString();
}
/// <summary>
/// PreProcessing.
/// Converts image data in SoftwareBitmap to TensorFloat.
/// </summary>
public static TensorFloat SoftwareBitmapToTensorFloat(SoftwareBitmap image)
{
int width = image.PixelWidth;
int height = image.PixelHeight;
using (BitmapBuffer buffer = image.LockBuffer(BitmapBufferAccessMode.Read))
{
using (var reference = buffer.CreateReference())
{
// Implementation Reference:
// https://github.com/Microsoft/Windows-Machine-Learning/issues/22
unsafe
{
((IMemoryBufferByteAccess)reference).GetBuffer(out byte *dataInBytes, out uint capacity);
long[] shape = { 1, 3, height, width };
float[] pCPUTensor = new float[3 * width * height];
for (int i = 0; i < capacity; i += 4)
{
int pixelInd = i / 4;
pCPUTensor[pixelInd] = (float)dataInBytes[i];
pCPUTensor[(height * width) + pixelInd] = (float)dataInBytes[i + 1];
pCPUTensor[(height * width * 2) + pixelInd] = (float)dataInBytes[i + 2];
}
float[] processedTensor = NormalizeFloatArray(pCPUTensor);
TensorFloat tensorFloats = TensorFloat.CreateFromArray(shape, processedTensor);
return(tensorFloats);
}
}
}
}
public static async Task <TensorFloat> GetAsTensorFloat(this IRandomAccessStream stream,
int imageSize, float[] mean = null, float[] std = null)
{
BitmapDecoder decoder = await BitmapDecoder.CreateAsync(stream);
var softwareBitmap = await decoder.GetSoftwareBitmapAsync();
softwareBitmap = SoftwareBitmap.Convert(softwareBitmap, BitmapPixelFormat.Rgba8, BitmapAlphaMode.Premultiplied);
WriteableBitmap innerBitmap = new WriteableBitmap(softwareBitmap.PixelWidth, softwareBitmap.PixelHeight);
softwareBitmap.CopyToBuffer(innerBitmap.PixelBuffer);
int channelSize = imageSize * imageSize;
using (var context = innerBitmap.GetBitmapContext())
{
int[] src = context.Pixels;
var normalized = new float[imageSize * imageSize * 3];
for (var x = 0; x < imageSize; x++)
{
for (var y = 0; y < imageSize; y++)
{
var color = innerBitmap.GetPixel(y, x);
float r, g, b;
r = color.R;
g = color.G;
b = color.B;
if (mean != null && std != null)
{
r /= 255f;
g /= 255f;
b /= 255f;
r = (r - mean[0]) / std[0];
g = (g - mean[1]) / std[1];
b = (b - mean[2]) / std[2];
}
var indexChannelR = (x * imageSize) + y;
var indexChannelG = indexChannelR + channelSize;
var indexChannelB = indexChannelG + channelSize;
normalized[indexChannelR] = r;
normalized[indexChannelG] = g;
normalized[indexChannelB] = b;
}
}
return(TensorFloat.CreateFromArray(new List <long>()
{
1, 3, imageSize, imageSize
}, normalized));
}
}
public IEnumerable <float> EstimateDepth(float[] inTensor)
{
var task = Task.Run(async() =>
{
return(await onnxModel.EvaluateAsync(
TensorFloat.CreateFromArray(new long[] { 1, 3, InputHeight, InputWidth }, inTensor)));
});
return(task.Result.GetAsVectorView());
}
private async void PredictHangul(VideoFrame inputimage)
{
Stopwatch sw = new Stopwatch();
sw.Start();
// convert to bgr8
SoftwareBitmap bitgray8 = SoftwareBitmap.Convert(inputimage.SoftwareBitmap, BitmapPixelFormat.Gray8);
var buff = new byte[64 * 64];
bitgray8.CopyToBuffer(buff.AsBuffer());
var fbuff = new float[4096];
for (int i = 0; i < 4096; i++)
{
fbuff[i] = (float)buff[i] / 255;
}
long[] shape = { 1, 4096 };
charInput.input00 = TensorFloat.CreateFromArray(shape, fbuff);
var dummy = new float[1];
long[] dummy_shape = { };
charInput.keep_prob = TensorFloat.CreateFromArray(dummy_shape, dummy);
//Evaluate the model
charOuput = await charModel.EvaluateAsync(charInput);
//Convert output to datatype
IReadOnlyList <float> VectorImage = charOuput.output00.GetAsVectorView();
IList <float> ImageList = VectorImage.ToList();
//Display top results
var topPred = ImageList.Select((value, index) => new { index, value })
.ToDictionary(pair => pair.index, pair => pair.value)
.OrderByDescending(key => key.Value)
.ToArray();
string topLabeltxt = "";
for (int i = 1; i < 6; i++)
{
var item = topPred[i];
Debug.WriteLine($"{item.Key}, {item.Value}, {charLabel[item.Key]}");
topLabeltxt += $"{charLabel[item.Key]} ";
}
numberLabel.Text = charLabel[topPred[0].Key];
topLabel.Text = topLabeltxt;
Debug.WriteLine($"process time = {sw.Elapsed}");
}
private static TensorFloat Normalize(byte[] src, System.Numerics.Vector3 mean, System.Numerics.Vector3 std, uint width, uint height)
{
var normalized = new float[src.Length / 4 * 3];
for (int i = 0; i < src.Length / 4; i++)
{
var val = src[i];
normalized[i * 3 + 0] = ((src[4 * i] / 255f) - mean.X) / std.X;
normalized[i * 3 + 1] = ((src[4 * i + 1] / 255f) - mean.Y) / std.Y;
normalized[i * 3 + 2] = ((src[4 * i + 2] / 255f) - mean.Z) / std.Z;
}
var shape = new List <long> {
3, width, height
};
return(TensorFloat.CreateFromArray(shape, normalized));
}
internal String Evaluate()
{
// input tensor shape is [1x4]
long[] shape = new long[2];
shape[0] = 1;
shape[1] = 4;
// set up the input tensor
float[] input_data = new float[4];
input_data[0] = _sepal_length;
input_data[1] = _sepal_width;
input_data[2] = _petal_length;
input_data[3] = _petal_width;
TensorFloat tensor_float = TensorFloat.CreateFromArray(shape, input_data);
// bind the tensor to "input"
var binding = new LearningModelBinding(_session);
binding.Bind("input", tensor_float);
// evaluate
var results = _session.Evaluate(binding, "");
// get the results
TensorFloat prediction = (TensorFloat)results.Outputs.First().Value;
var prediction_data = prediction.GetAsVectorView();
// find the highest predicted value
int max_index = 0;
float max_value = 0;
for (int i = 0; i < prediction_data.Count; i++)
{
var val = prediction_data.ElementAt(i);
if (val > max_value)
{
max_value = val;
max_index = i;
}
}
// return the label corresponding to the highest predicted value
return(_labels.ElementAt(max_index));
}
public override List <ONNXTensor> Run(IEnumerable <string> outputs, Dictionary <string, ONNXTensor> feedDict)
{
var binding = new LearningModelBinding(sess);
foreach (var item in feedDict)
{
object tensor;
if (!IsFP16)
{
tensor = TensorFloat.CreateFromArray(item.Value.Shape, item.Value.Buffer);
if (IsGPU)
{
//TODO: Move SoftwareTensor to DX12Tensor
tensor = MoveToGPU((TensorFloat)tensor);
}
}
else
{
tensor = TensorFloat16Bit.CreateFromArray(item.Value.Shape, item.Value.Buffer);
}
binding.Bind(item.Key, tensor);
}
var result = sess.Evaluate(binding, $"eval{++evalCount}");
var ret = new List <ONNXTensor>();
foreach (var item in outputs)
{
var tensor = result.Outputs[item] as TensorFloat;
var vector = tensor.GetAsVectorView().ToArray();
ret.Add(new ONNXTensor()
{
Buffer = vector, Shape = tensor.Shape.ToArray()
});
}
return(ret);
}
private async void Button_Click(object sender, RoutedEventArgs e)
{
var shape = new long[] { 1, 1 };
var modelInput = new taxiFarePredInput()
{
PassengerCount = TensorFloat.CreateFromArray(shape, new float[] { 1f }),
TripTime = TensorFloat.CreateFromArray(shape, new float[] { 1140f }),
TripDistance = TensorFloat.CreateFromArray(shape, new float[] { 3.75f }),
FareAmount = TensorFloat.CreateFromArray(shape, new float[] { 0f }),
};
var modelFile = await StorageFile.GetFileFromApplicationUriAsync(new Uri("ms-appx:///Assets/taxiFarePred.onnx"));
var session = await taxiFarePredModel.CreateFromStreamAsync(modelFile);
var modelOutput = await session.EvaluateAsync(modelInput);
var score = modelOutput.Score0.GetAsVectorView();
btn1.Content = score[0];
}
private async Task <List <float> > ArcFace(SoftwareBitmap softwareBitmap)
{
// Encapsulate the image within a VideoFrame to be bound and evaluated
VideoFrame inputImage = VideoFrame.CreateWithSoftwareBitmap(softwareBitmap);
int height = inputImage.SoftwareBitmap.PixelHeight;
int width = inputImage.SoftwareBitmap.PixelWidth;
float[] data = new float[1 * 3 * ARC_FACE_INPUT * ARC_FACE_INPUT];
byte[] imageBytes = new byte[4 * height * width];
inputImage.SoftwareBitmap.CopyToBuffer(imageBytes.AsBuffer());
int id = 0;
for (int i = 0; i < data.Length; i += 4)
{
float blue = (float)imageBytes[i];
float green = (float)imageBytes[i + 1];
float red = (float)imageBytes[i + 2];
data[id++] = blue;
data[id++] = green;
data[id++] = red;
}
_arcFaceInput.data = TensorFloat.CreateFromArray(new List <long> {
1, 3, ARC_FACE_INPUT, ARC_FACE_INPUT
}, data);
// Process the frame with the model
_arcFaceOutput = await _arcFaceModel.EvaluateAsync(_arcFaceInput);
IReadOnlyList <float> vectorImage = _arcFaceOutput.fc1.GetAsVectorView();
return(vectorImage.ToList());
}
static async Task <int> Main(string[] args)
{
try
{
//
// Parse options
//
Options = new AppOptions();
Options.Parse(args);
if (Options.ShowList)
{
}
if (Options.Exit)
{
return(-1);
}
if (string.IsNullOrEmpty(Options.FileName))
{
throw new ApplicationException("Please use --file to specify which file to use");
}
//
// Init module client
//
if (Options.UseEdge)
{
Log.WriteLine($"{AppOptions.AppName} module starting.");
await BlockTimer("Initializing Azure IoT Edge", async() => await InitEdge());
}
cts = new CancellationTokenSource();
AssemblyLoadContext.Default.Unloading += (ctx) => cts.Cancel();
Console.CancelKeyPress += (sender, cpe) => cts.Cancel();
//
// Load model
//
MLModel model = null;
Console.WriteLine($"Loading model from: '{Options.ModelPath}', Exists: '{File.Exists(Options.ModelPath)}'");
await BlockTimer($"Loading modelfile '{Options.ModelPath}' on the {(Options.UseGpu ? "GPU" : "CPU")}",
async() =>
{
var d = Directory.GetCurrentDirectory();
var path = d + "\\" + Options.ModelPath;
StorageFile modelFile = await AsAsync(StorageFile.GetFileFromPathAsync(path));
model = await MLModel.CreateFromStreamAsync(modelFile);
});
do
{
//
// Open file
//
var rows = new List <DataRow>();
try
{
using (var fs = new StreamReader(Options.FileName))
{
// I just need this one line to load the records from the file in my List<CsvLine>
rows = new CsvHelper.CsvReader(fs).GetRecords <DataRow>().ToList();
Console.WriteLine($"Loaded {rows.Count} row(s)");
}
}
catch (Exception e)
{
Console.WriteLine(e);
}
Console.WriteLine(rows);
//
// Main loop
//
foreach (var row in rows)
{
//
// Evaluate model
//
var inputShape = new long[2] {
1, 4
};
var inputFeatures = new float[4] {
row.Temperature, row.Pressure, row.Humidity, row.ExternalTemperature
};
MLModelVariable result = null;
var evalticks = await BlockTimer("Running the model",
async() =>
{
result = await model.EvaluateAsync(new MLModelVariable()
{
Variable = TensorFloat.CreateFromArray(inputShape, inputFeatures)
});
});
//
// Print results
//
var message = new MessageBody
{
result = result.Variable.GetAsVectorView().First()
};
message.metrics.evaltimeinms = evalticks;
var json = JsonConvert.SerializeObject(message);
Log.WriteLineRaw($"Recognized {json}");
//
// Send results to Edge
//
if (Options.UseEdge)
{
var eventMessage = new Message(Encoding.UTF8.GetBytes(json));
await ioTHubModuleClient.SendEventAsync("resultsOutput", eventMessage);
// Let's not totally spam Edge :)
await Task.Delay(500);
}
Console.WriteLine("Waiting 1 second...");
Thread.Sleep(1000);
}
}while (Options.RunForever && !cts.Token.IsCancellationRequested);
}
catch (Exception ex)
{
Console.WriteLine(ex);
return(-1);
}
return(0);
}
static SoftwareBitmap GetMelspectrogramFromSignal(
IEnumerable <float> rawSignal,
int batchSize = 1,
int windowSize = 256,
int dftSize = 256,
int hopSize = 3,
int nMelBins = 1024,
int samplingRate = 8192,
int amplitude = 5000
)
{
float[] signal = rawSignal.ToArray();
//Scale the signal by a given amplitude
for (int i = 0; i < signal.Length; i++)
{
signal[i] = signal[i] * amplitude;
}
int signalSize = signal.Length;
var nDFT = 1 + (signalSize - dftSize) / hopSize;
var onesidedDftSize = (dftSize >> 1) + 1;
long[] signalShape = { batchSize, signalSize };
long[] melSpectrogramShape = { batchSize, 1, nDFT, nMelBins };
var builder = LearningModelBuilder.Create(13)
.Inputs.Add(LearningModelBuilder.CreateTensorFeatureDescriptor("Input.TimeSignal", TensorKind.Float, signalShape))
.Outputs.Add(LearningModelBuilder.CreateTensorFeatureDescriptor("Output.MelSpectrogram", TensorKind.Float, melSpectrogramShape))
.Operators.Add(new Operator("HannWindow", MicrosoftExperimentalDomain)
.SetConstant("size", TensorInt64Bit.CreateFromArray(new List <long>(), new long[] { windowSize }))
.SetOutput("output", "hann_window"))
.Operators.Add(new Operator("STFT", MicrosoftExperimentalDomain)
.SetName("STFT_NAMED_NODE")
.SetInput("signal", "Input.TimeSignal")
.SetInput("window", "hann_window")
.SetConstant("frame_length", TensorInt64Bit.CreateFromArray(new List <long>(), new long[] { dftSize }))
.SetConstant("frame_step", TensorInt64Bit.CreateFromArray(new List <long>(), new long[] { hopSize }))
.SetOutput("output", "stft_output"))
.Operators.Add(new Operator("ReduceSumSquare")
.SetInput("data", "stft_output")
.SetAttribute("axes", TensorInt64Bit.CreateFromArray(new List <long>()
{
1
}, new long[] { 3 }))
.SetAttribute("keepdims", TensorInt64Bit.CreateFromArray(new List <long>(), new long[] { 0 }))
.SetOutput("reduced", "magnitude_squared"))
.Operators.Add(new Operator("Div")
.SetInput("A", "magnitude_squared")
.SetConstant("B", TensorFloat.CreateFromArray(new List <long>(), new float[] { dftSize }))
.SetOutput("C", "power_frames"))
.Operators.Add(new Operator("MelWeightMatrix", MicrosoftExperimentalDomain)
.SetConstant("num_mel_bins", TensorInt64Bit.CreateFromArray(new List <long>(), new long[] { nMelBins }))
.SetConstant("dft_length", TensorInt64Bit.CreateFromArray(new List <long>(), new long[] { dftSize }))
.SetConstant("sample_rate", TensorInt64Bit.CreateFromArray(new List <long>(), new long[] { samplingRate }))
.SetConstant("lower_edge_hertz", TensorFloat.CreateFromArray(new List <long>(), new float[] { 0 }))
.SetConstant("upper_edge_hertz", TensorFloat.CreateFromArray(new List <long>(), new float[] { (float)(samplingRate / 2.0) }))
.SetOutput("output", "mel_weight_matrix"))
.Operators.Add(new Operator("Reshape")
.SetInput("data", "power_frames")
.SetConstant("shape", TensorInt64Bit.CreateFromArray(new List <long>()
{
2
}, new long[] { batchSize *nDFT, onesidedDftSize }))
.SetOutput("reshaped", "reshaped_output"))
.Operators.Add(new Operator("MatMul")
.SetInput("A", "reshaped_output")
.SetInput("B", "mel_weight_matrix")
.SetOutput("Y", "mel_spectrogram"))
.Operators.Add(new Operator("Reshape")
.SetInput("data", "mel_spectrogram")
.SetConstant("shape", TensorInt64Bit.CreateFromArray(new List <long>()
{
4
}, melSpectrogramShape))
.SetOutput("reshaped", "Output.MelSpectrogram"));
var model = builder.CreateModel();
LearningModelSession session = new LearningModelSession(model);
LearningModelBinding binding = new LearningModelBinding(session);
// Bind input
binding.Bind("Input.TimeSignal", TensorFloat.CreateFromArray(signalShape, signal));
// Bind output
var outputImage = new VideoFrame(
BitmapPixelFormat.Bgra8,
nMelBins,
nDFT);
binding.Bind("Output.MelSpectrogram", outputImage);
// Evaluate
var sw = Stopwatch.StartNew();
var result = session.Evaluate(binding, "");
sw.Stop();
Console.WriteLine("Evaluate Took: %f\n", sw.ElapsedMilliseconds);
return(outputImage.SoftwareBitmap);
}
public TensorFloat grid; // shape(-1,125,13,13)
public TinyYoloV1_2Output()
{
var tensorFloat = TensorFloat.CreateFromArray(new long[] { -1, 125, 13, 13 }, new float[21125]);
grid = tensorFloat;
}
/// <summary>
/// タイマイベント
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private async void TimerCapFlame(object sender)
{
//複数スレッドでの同時実行を抑制
if (!semaphore.Wait(0))
{
return;
}
else if (this.ModelGen == null)
{
semaphore.Release();
return;
}
try
{
//AIモデルのインプットデータは解像度224x224,BGRA8にする必要がある。
BitmapPixelFormat InputPixelFormat = BitmapPixelFormat.Bgra8;
using (VideoFrame previewFrame = new VideoFrame(InputPixelFormat, 640, 480, BitmapAlphaMode.Ignore))
{
//フレームを取得
await this.mediaCapture.GetPreviewFrameAsync(previewFrame);
if (previewFrame != null) //フレームを正しく取得できた時
{
//モデルへのデータ入力クラスでインスタンスを作成する
var modelInput = new Input();
//SoftwareBitmapを作成
SoftwareBitmap bitmapBuffer = new SoftwareBitmap(BitmapPixelFormat.Bgra8, 224, 224, BitmapAlphaMode.Ignore);
//SoftwareBitmapでVideoFrameを作成する
VideoFrame buffer = VideoFrame.CreateWithSoftwareBitmap(bitmapBuffer);
//キャプチャしたフレームを作成したVideoFrameへコピーする
await previewFrame.CopyToAsync(buffer);
//SoftwareBitmapを取得する（これはリサイズ済みになる）
SoftwareBitmap resizedBitmap = buffer.SoftwareBitmap;
//WritableBitmapへ変換する
WriteableBitmap innerBitmap = null;
byte[] buf = null;
await Dispatcher.RunAsync(Windows.UI.Core.CoreDispatcherPriority.Normal, async() => {
innerBitmap = new WriteableBitmap(resizedBitmap.PixelWidth, resizedBitmap.PixelHeight);
resizedBitmap.CopyToBuffer(innerBitmap.PixelBuffer);
buf = new byte[innerBitmap.PixelBuffer.Length];
innerBitmap.PixelBuffer.CopyTo(buf);
});
//バッファへコピーする
//innerBitmap.PixelBuffer.CopyTo(buf);6
SoftwareBitmap sb = SoftwareBitmap.CreateCopyFromBuffer(buf.AsBuffer(), BitmapPixelFormat.Bgra8, 224, 224, BitmapAlphaMode.Ignore);
//取得画像をコントロールに表示する
await this.Dispatcher.RunAsync(Windows.UI.Core.CoreDispatcherPriority.Normal, async() =>
{
var src = new SoftwareBitmapSource();
//await src.SetBitmapAsync(previewFrame.SoftwareBitmap);
await src.SetBitmapAsync(sb);
Image_CapImg.Source = src;
});
//画像のアルファチャンネル削除と配列形状変更
byte[] buf2 = ConvertImageaArray(buf);
//正規化しつつfloat配列に変換する
float[] inData = NormalizeImage(buf2);
//入力用のテンソルを作成（Windows.AI.MachineLearning.TensorFloatクラス）
TensorFloat tf =
TensorFloat.CreateFromArray(new long[] { 1, 3, 224, 224 }, inData);
//入力フォーマットに合わせたデータをセットする
Input indata = new Input();
indata.data = tf;
modelInput.data = tf;
//AIモデルにデータを渡すと推定値の入ったリストが返る
//ModelOutput = await ModelGen.EvaluateAsync(modelInput);
var output = await ModelGen.EvaluateAsync(modelInput);
//UIスレッドに結果を表示
await this.Dispatcher.RunAsync(Windows.UI.Core.CoreDispatcherPriority.Normal, () =>
{
//予測結果を表示
//string label = outputData.classLabel[0];
var result_vec = output.mobilenetv20_output_flatten0_reshape0.GetAsVectorView();
var list = result_vec.ToArray <float>();
var max1st = list.Max();
var index1st = Array.IndexOf(list, max1st); //最大確立のインデックスを取得
string ans = classList.Classes[index1st].ToString();
//result = result + "Class: " + label + ", Prob: " + ModelOutput.prob_1[label];
//結果表示
this.Text_Result_1st.Text = ans + ":" + max1st.ToString("0.0");
});
}
}
}
catch (Exception ex)
{
Debug.WriteLine("周期処理で例外発生");
Debug.WriteLine(ex.ToString());
}
finally
{
semaphore.Release();
}
}
