настройка скорости обучения с помощью графического процессора CNTK 2.1.0, VS 2015, обновление 3, C ++, Window

Я сослалсяhttps://github.com/Microsoft/CNTK/blob/master/Tests/UnitTests/V2LibraryTests/FeedForwardTests.cpp«

В функции «TestFeedForwardNetworkCreation» я не могу найти способ установки скорости обучения.

    std::vector<float> inputData(inputDim * numSamples);
for (size_t i2 = 0; i2 < inputData.size(); ++i2)
inputData[i2] = ((float)rand()) / RAND_MAX;

NDShape inputShape = inputVar.Shape().AppendShape({ 1, numSamples });
ValuePtr inputValue = MakeSharedObject<Value>(MakeSharedObject<NDArrayView>(inputShape, inputData.data(), inputData.size(), DeviceDescriptor::CPUDevice(), true));

std::vector<float> labelData(numOutputClasses * numSamples, 0);
for (size_t i3 = 0; i3 < numSamples; ++i3)
labelData[(i3*numOutputClasses) + (rand() % numOutputClasses)] = 1;

NDShape labelShape = labelsVar.Shape().AppendShape({ 1, numSamples });
ValuePtr labelValue = MakeSharedObject<Value>(MakeSharedObject<NDArrayView>(labelShape, labelData.data(), labelData.size(), DeviceDescriptor::CPUDevice(), true));

ValuePtr outputValue, predictionErrorValue;
std::unordered_map<Variable, ValuePtr> outputs = { { classifierOutput, outputValue }, { prediction, predictionErrorValue } };
auto backpropState = ffNet->Forward({ { inputVar, inputValue }, { labelsVar, labelValue } }, outputs, device, { trainingLoss });

// Perform backprop
NDShape outputShape = trainingLoss->Output().Shape();
std::vector<float> rootGradientsData(outputShape.TotalSize(), 1);
ValuePtr rootGradientValue = MakeSharedObject<Value>(MakeSharedObject<NDArrayView>(outputShape, rootGradientsData.data(), rootGradientsData.size(), DeviceDescriptor::CPUDevice(), true));
std::unordered_map<Variable, ValuePtr> paramGradients;
auto allParams = ffNet->Parameters();
for (auto iter = allParams.begin(); iter != allParams.end(); ++iter)
paramGradients[*iter] = nullptr;
ffNet->Backward(backpropState, { { trainingLoss, rootGradientValue } }, paramGradients);