本文整理汇总了C#中INetwork.SetLearningRate方法的典型用法代码示例。如果您正苦于以下问题：C# INetwork.SetLearningRate方法的具体用法？C# INetwork.SetLearningRate怎么用？C# INetwork.SetLearningRate使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类INetwork的用法示例。
在下文中一共展示了INetwork.SetLearningRate方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: Optimiser
//.........这里部分代码省略.........
results = new float[Convert.ToInt32(dataAccess.GetParameter("Opt_Global_BufferSize"))];
if (useBP)
{
int learningRateFunction = Convert.ToInt32(dataAccess.GetParameter("Opt_Bp_LearningType"));
double initialLR = Convert.ToDouble(dataAccess.GetParameter("Opt_Bp_InitialLearnRate"));
double finalLR = Convert.ToDouble(dataAccess.GetParameter("Opt_Bp_FinalLearnRate"));
int jitterEpoch = Convert.ToInt32(dataAccess.GetParameter("Opt_Bp_JitterEpoch"));
double jitterNoiseLimit = Convert.ToDouble(dataAccess.GetParameter("Opt_Bp_JitterNoiseLimit"));
NeuronDotNet.Core.Backpropagation.LinearLayer inputLayer = new NeuronDotNet.Core.Backpropagation.LinearLayer(preprocessor.ImageSize.Width * preprocessor.ImageSize.Height);
NeuronDotNet.Core.Backpropagation.SigmoidLayer hiddenLayer = new NeuronDotNet.Core.Backpropagation.SigmoidLayer(total);
hiddenLayer.InputGroups = inputGroups.Length;
NeuronDotNet.Core.Backpropagation.SigmoidLayer outputLayer = new NeuronDotNet.Core.Backpropagation.SigmoidLayer(1);
hiddenLayer.Initializer = new NguyenWidrowFunction();
new BackpropagationConnector(
inputLayer,
hiddenLayer,
inputGroups,
preprocessor.ImageSize.Width,
preprocessor.ImageSize.Height
);
new BackpropagationConnector(hiddenLayer, outputLayer);
network = new BackpropagationNetwork(inputLayer, outputLayer);
switch (learningRateFunction)
{
case 0:
network.SetLearningRate(initialLR);
break;
case 1:
network.SetLearningRate(new NeuronDotNet.Core.LearningRateFunctions.ExponentialFunction(initialLR, finalLR));//exp
break;
case 2:
network.SetLearningRate(new NeuronDotNet.Core.LearningRateFunctions.HyperbolicFunction(initialLR, finalLR));//hyp
break;
case 3:
network.SetLearningRate(new NeuronDotNet.Core.LearningRateFunctions.LinearFunction(initialLR, finalLR));//lin
break;
default:
throw new ArgumentOutOfRangeException("The learning rate index is out of range.\n");
}
network.JitterEpoch = jitterEpoch;
network.JitterNoiseLimit = jitterNoiseLimit;
}
if (usePSO)
{
double minP = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_MinP"));
double maxP = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_MaxP"));
double minI = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_MinI"));
double maxI = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_MaxI"));
double quant = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_Quant"));
double vMax = Convert.ToDouble(dataAccess.GetParameter("Opt_Pso_vMax"));
int clamping = Convert.ToInt32(dataAccess.GetParameter("Opt_Pso_Clamping"));
int initLinks = Convert.ToInt32(dataAccess.GetParameter("Opt_Pso_InitLinks"));
int randomness = Convert.ToInt32(dataAccess.GetParameter("Opt_Pso_Randomness"));
int randOrder = Convert.ToInt32(dataAccess.GetParameter("Opt_Pso_ParticleOrder"));