private void SaveScreenParametersToCurrentNetwork()
{
// 训练周期
currentProject.LearningParameters.MaxTrainingCycles = int.Parse(txtCycles.Text);
// 获得学习率
currentProject.LearningParameters.InitialLearningRate = double.Parse(txtInitialLearningRate.Text);
if (txtFinalLearningRate.Text.Length > 0)
{
currentProject.LearningParameters.FinalLearningRate = double.Parse(txtFinalLearningRate.Text);
}
else
{
currentProject.LearningParameters.FinalLearningRate = null;
}
// 是在组合中选择LRF吗？
if (comboLRFunction.SelectedIndex > 0)
{
currentProject.LearningParameters.LearningRateFunction = (LearningRateFunction)comboLRFunction.SelectedItem;
}
else
{
currentProject.LearningParameters.LearningRateFunction = null;
}
// 我们可以使用LRF吗？ 需要两个学习率
if (comboLRFunction.SelectedIndex > 0 && currentProject.LearningParameters.FinalLearningRate.HasValue)
{
// 设置学习率函数
currentProject.Network.SetLearningRate(
LearningRateFactory.GetLearningRateFunction((LearningRateFunction)comboLRFunction.SelectedItem,
currentProject.LearningParameters.InitialLearningRate,
currentProject.LearningParameters.FinalLearningRate.Value)
);
}
else
{
// 设置学习率
if (currentProject.LearningParameters.FinalLearningRate.HasValue)
{
currentProject.Network.SetLearningRate(currentProject.LearningParameters.InitialLearningRate, currentProject.LearningParameters.FinalLearningRate.Value);
}
else
{
currentProject.Network.SetLearningRate(currentProject.LearningParameters.InitialLearningRate);
}
}
// 在连接器中设置动量（0未定义）
double momentum = 0;
if (textMomentum.Text.Length > 0)
{
momentum = double.Parse(textMomentum.Text);
}
foreach (ILayer layer in currentProject.Network.Layers)
{
foreach (BackpropagationConnector conn in layer.SourceConnectors)
{
conn.Momentum = momentum;
}
foreach (BackpropagationConnector conn in layer.TargetConnectors)
{
conn.Momentum = momentum;
}
}
}
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; }
}