public List <ActivationLayer> HiddenLayerList()
{
List <ActivationLayer> ActivationLayerList = new List <ActivationLayer>();
for (int i = 0; i < neuronCount.Count; i++)
{
if (neuronCount[i] < 1)
{
neuronCount[i] = 1;
}
;
if (layerStructure[i] == 0)
{
SigmoidLayer currenthiddenLayer = new SigmoidLayer(neuronCount[i]); ActivationLayerList.Add(currenthiddenLayer);
}
else if (layerStructure[i] == 1)
{
LinearLayer currenthiddenLayer = new LinearLayer(neuronCount[i]); ActivationLayerList.Add(currenthiddenLayer);
}
else if (layerStructure[i] == 2)
{
LogarithmLayer currenthiddenLayer = new LogarithmLayer(neuronCount[i]); ActivationLayerList.Add(currenthiddenLayer);
}
else if (layerStructure[i] == 3)
{
SineLayer currenthiddenLayer = new SineLayer(neuronCount[i]); ActivationLayerList.Add(currenthiddenLayer);
}
else if (layerStructure[i] == 4)
{
TanhLayer currenthiddenLayer = new TanhLayer(neuronCount[i]); ActivationLayerList.Add(currenthiddenLayer);
}
else
{
return(new List <ActivationLayer>());
}
}
return(ActivationLayerList);
}
public void shrit(bool all = true)
{
List <int> referee = new List <int>();
foreach (var item in layers)
{
referee.Add(item);
}
referee.Add(opt);
LinearLayer inputLayer = new LinearLayer(npu);
ActivationLayer last = null;
for (int i = 0; i < layers.Count; i++)
{
ActivationLayer hiddenLayer = null;
switch (valf)
{
case acti.Sigmoid:
hiddenLayer = new SigmoidLayer(layers[0]);
break;
case acti.tanh:
hiddenLayer = new TanhLayer(layers[0]);
break;
case acti.Logarith:
hiddenLayer = new LogarithmLayer(layers[0]);
break;
case acti.Sine:
hiddenLayer = new SineLayer(layers[0]);
break;
case acti.Linear:
hiddenLayer = new LinearLayer(layers[0]);
break;
default:
break;
}
if (last == null)
{
new BackpropagationConnector(inputLayer, hiddenLayer);
}
else
{
new BackpropagationConnector(last, hiddenLayer);
}
last = hiddenLayer;
}
ActivationLayer outputLayer = new SigmoidLayer(opt);
if (last != null)
{
new BackpropagationConnector(last, outputLayer);
}
else
{
new BackpropagationConnector(inputLayer, outputLayer);
}
netamp = new BackpropagationNetwork(inputLayer, outputLayer);
}
private void buttonOK_Click(object sender, EventArgs e)
{
this.Cursor = Cursors.WaitCursor;
try
{
//
// 解析数据文件
//
int inputCount = 0;
int outputCount = 1;
if (textInputCount.Text.Length > 0)
{
inputCount = int.Parse(textInputCount.Text);
}
if (textOutputCount.Text.Length > 0)
{
outputCount = int.Parse(textOutputCount.Text);
}
TrainingSet trainingSet = DataFile.CsvFileToTrainingSet(textTrainingSet.Text, ref inputCount, ref outputCount);
TrainingSet crossvalidSet = null;
if (textCvSet.Text.Length > 0)
{
crossvalidSet = DataFile.CsvFileToTrainingSet(textCvSet.Text, ref inputCount, ref outputCount);
}
//
// 创建新网络
//
// 输入层始终与输入计数是线性关系
LinearLayer inputLayer = new LinearLayer(inputCount);
// 创建隐层
ActivationLayer hiddenLayer1 = null;
ActivationLayer hiddenLayer2 = null;
ActivationLayer outputLayer = null;
if (comboActFunction1.SelectedIndex < 0)
{
MessageBox.Show("请选择激活函数！"); return;
}
switch ((HiddenLayerType)comboActFunction1.SelectedItem)
{
case HiddenLayerType.Linear: hiddenLayer1 = new LinearLayer(int.Parse(textNeuronCount1.Text)); break;
case HiddenLayerType.Logarithmic: hiddenLayer1 = new LogarithmLayer(int.Parse(textNeuronCount1.Text)); break;
case HiddenLayerType.Sigmoid: hiddenLayer1 = new SigmoidLayer(int.Parse(textNeuronCount1.Text)); break;
case HiddenLayerType.Sine: hiddenLayer1 = new SineLayer(int.Parse(textNeuronCount1.Text)); break;
case HiddenLayerType.Tanh: hiddenLayer1 = new TanhLayer(int.Parse(textNeuronCount1.Text)); break;
}
if (textNeuronCount2.Text.Length > 0 && int.Parse(textNeuronCount2.Text) > 0)
{
switch ((HiddenLayerType)comboActFunction2.SelectedItem)
{
case HiddenLayerType.Linear: hiddenLayer2 = new LinearLayer(int.Parse(textNeuronCount2.Text)); break;
case HiddenLayerType.Logarithmic: hiddenLayer2 = new LogarithmLayer(int.Parse(textNeuronCount2.Text)); break;
case HiddenLayerType.Sigmoid: hiddenLayer2 = new SigmoidLayer(int.Parse(textNeuronCount2.Text)); break;
case HiddenLayerType.Sine: hiddenLayer2 = new SineLayer(int.Parse(textNeuronCount2.Text)); break;
case HiddenLayerType.Tanh: hiddenLayer2 = new TanhLayer(int.Parse(textNeuronCount2.Text)); break;
}
}
if (comboOutputFunction.SelectedIndex < 0)
{
MessageBox.Show("请选择输出函数！"); return;
}
switch ((HiddenLayerType)comboOutputFunction.SelectedItem)
{
case HiddenLayerType.Linear: outputLayer = new LinearLayer(outputCount); break;
case HiddenLayerType.Logarithmic: outputLayer = new LogarithmLayer(outputCount); break;
case HiddenLayerType.Sigmoid: outputLayer = new SigmoidLayer(outputCount); break;
case HiddenLayerType.Sine: outputLayer = new SineLayer(outputCount); break;
case HiddenLayerType.Tanh: outputLayer = new TanhLayer(outputCount); break;
}
// 连接层, hidden2是可选的
new BackpropagationConnector(inputLayer, hiddenLayer1);
if (hiddenLayer2 != null)
{
new BackpropagationConnector(hiddenLayer1, hiddenLayer2);
new BackpropagationConnector(hiddenLayer2, outputLayer);
}
else
{
new BackpropagationConnector(hiddenLayer1, outputLayer);
}
BackpropagationNetwork backpropNetwork = new BackpropagationNetwork(inputLayer, outputLayer);
//
// 设置学习和退出参数
//
double startLearningRate = double.Parse(textStartLearningRate.Text);
double?finalLearningRate = null;
if (textFinalLearningRate.Text.Length > 0)
{
finalLearningRate = double.Parse(textFinalLearningRate.Text);
}
// 如果选择了学习率函数则使用
LearningRateFunction?lrf = null;
if (comboLRFunction.SelectedIndex > 0)
{
lrf = (LearningRateFunction)comboLRFunction.SelectedItem;
backpropNetwork.SetLearningRate(
LearningRateFactory.GetLearningRateFunction(lrf.Value, startLearningRate, finalLearningRate.Value));
}
else
{
// 否则使用普通学习率，也许有起点和终点
if (finalLearningRate.HasValue)
{
backpropNetwork.SetLearningRate(startLearningRate, finalLearningRate.Value);
}
else
{
backpropNetwork.SetLearningRate(startLearningRate);
}
}
// 如果给定，在连接器中设置动量
double?momentum = null;
if (textMomentum.Text.Length > 0)
{
momentum = double.Parse(textMomentum.Text);
foreach (ILayer layer in backpropNetwork.Layers)
{
foreach (BackpropagationConnector conn in layer.SourceConnectors)
{
conn.Momentum = momentum.Value;
}
foreach (BackpropagationConnector conn in layer.TargetConnectors)
{
conn.Momentum = momentum.Value;
}
}
}
//
// 新建工程和保存工程
//
int tmpInt;
NewProject = new NnProject();
NewProject.Network = backpropNetwork;
// 确保为新网络初始化权重，默认情况下新的训练周期将不会初始化
NewProject.Network.Initialize();
NewProject.ProjectName = textProjectName.Text.Trim();
NewProject.SaveFolder = textSaveFolder.Text;
NewProject.TrainingSet = trainingSet;
NewProject.CrossValidationSet = crossvalidSet;
NewProject.LearningParameters = new NnProject.NnLearningParameters();
NewProject.LearningParameters.InitialLearningRate = startLearningRate;
NewProject.LearningParameters.FinalLearningRate = finalLearningRate;
NewProject.LearningParameters.LearningRateFunction = lrf;
NewProject.LearningParameters.Momentum = momentum;
if (int.TryParse(textTrainingCycles.Text, out tmpInt))
{
NewProject.LearningParameters.MaxTrainingCycles = tmpInt;
}
NnProject.Save(NewProject, textSaveFolder.Text);
this.Close();
}
catch (Exception ex)
{
this.Cursor = Cursors.Default;
MessageBox.Show("Error creating network - " + ex.Message, "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
finally
{ this.Cursor = Cursors.Default; }
}
