public void Train(int cpi, int cs = 5, int seed = -1)
{
if (seed == -1)
{
seed = Environment.TickCount;
}
Random r = new Random(seed);
int ai = 0;
TrainingSet ts = new TrainingSet(Inputs, W * H * 3);
foreach (var i in InImgs)
{
double[] iv = new double[Inputs];
double[] ov = new double[W * H * 3];
int ic = 0;
for (int y = 0; y < i.H; y++)
{
for (int x = 0; x < i.W; x++)
{
iv[ic] = GV(i.Dat[ic++]);
iv[ic] = GV(i.Dat[ic++]);
iv[ic] = GV(i.Dat[ic++]);
}
}
Image oi = OutImgs[ai];
int vv = 0;
for (int y = 0; y < i.H; y++)
{
for (int x = 0; x < i.W; x++)
{
//int l = (i.H * y * 3) + (x * 3);
ov[vv] = GV(i.Dat[vv++]);
ov[vv] = GV(i.Dat[vv++]);
ov[vv] = GV(i.Dat[vv++]);
}
}
ai++;
TrainingSample s = new TrainingSample(iv, ov);
for (int xc = 0; xc < cpi; xc++)
{
ts.Add(s);
}
}
Ready = false;
//for(int t = 0; t < cs; t++)
//{
// net.BeginEpochEvent += TrainE;
net.EndEpochEvent += EndE;
net.Learn(ts, cs);
net.StopLearning();
Console.WriteLine("Done training mind.");
}
/// <summary>
/// 点击计算按钮
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void tsmiCalculate_Click(object sender, EventArgs e)
{
// 创建输入层、隐层和输出层
ActivationLayer inputLayer = GetLayer(cboInputLayerType.SelectedItem.ToString(), 2);
ActivationLayer hiddenLayer = GetLayer(cboHiddenLayerType.SelectedItem.ToString(), int.Parse(txtHiddenLayerCount.Text));
ActivationLayer outputLayer = GetLayer(cboOutputLayerType.SelectedItem.ToString(), 1);
// 创建层之间的关联
new BackpropagationConnector(inputLayer, hiddenLayer, ConnectionMode.Complete).Initializer = new RandomFunction(0, 0.3);
new BackpropagationConnector(hiddenLayer, outputLayer, ConnectionMode.Complete).Initializer = new RandomFunction(0, 0.3);
// 创建神经网络
var network = new BackpropagationNetwork(inputLayer, outputLayer);
network.SetLearningRate(double.Parse(txtInitialLearningRate.Text), double.Parse(txtFinalLearningRate.Text));
// 进行训练
var trainingSet = new TrainingSet(2, 1);
for (var i = 0; i < 17; i++)
{
var x1 = data[i, 0];
var x2 = data[i, 1];
var y = data[i, 2];
var inputVector = new double[] { x1, x2 };
var outputVector = new double[] { y };
var trainingSample = new TrainingSample(inputVector, outputVector);
trainingSet.Add(trainingSample);
}
network.SetLearningRate(0.3, 0.1);
network.Learn(trainingSet, int.Parse(txtTrainingEpochs.Text));
network.StopLearning();
// 进行预测
for (var i = 0; i < 17; i++)
{
var x1 = data[i, 0];
var x2 = data[i, 1];
var y = data[i, 2];
var testInput = new double[] { x1, x2 };
var testOutput = network.Run(testInput)[0];
var absolute = testOutput - y;
var relative = Math.Abs((testOutput - y) / testOutput);
dgvData.Rows[i].Cells[3].Value = testOutput.ToString("f3");
dgvData.Rows[i].Cells[4].Value = absolute.ToString("f3");
dgvData.Rows[i].Cells[5].Value = (relative * 100).ToString("f1") + "%";
}
}
private void StopLearning(object sender, EventArgs e)
{
if (network != null)
{
network.StopLearning();
LineItem lineItem = new LineItem("Approximated Function");
for (double xVal = 0; xVal < 10; xVal += 0.05d)
{
lineItem.AddPoint(xVal, network.Run(new double[] { xVal })[0]);
}
lineItem.Symbol.Type = SymbolType.None;
lineItem.Color = Color.DarkOrchid;
functionGraph.GraphPane.CurveList.Add(lineItem);
functionGraph.Refresh();
functionGraph.GraphPane.CurveList.Remove(lineItem);
}
network = null;
EnableControls(true);
}
