public void StartTrain(int epoch)
{
Sources = new CnnSource(epoch);
Sources.CollectImgInfo(@"D:\AI\test_img", @"D:\AI\test_img2");
Tuple <double[, , , ], double[, ]> Train = Sources.GetNextEpoch();
MapInput = Train.Item1;
LMatrix lable = Train.Item2;
#region 初始化隐藏层基本属性和隐藏层权重值
var layers = new LayerAttribute[] {
new LayerAttribute
{
Count = 26,
Depth = 3
},
new LayerAttribute
{
Count = 52,
Depth = 26
},
new LayerAttribute
{
Count = 52,
Depth = 38
}
};
LayerInfo = new LayerParamsInfo[layers.Length];
for (int index = 0; index < layers.Length; index++)
{
LayerInfo[index] = new LayerParamsInfo();
LayerInfo[index].CurrentLayer = index;
LayerInfo[index].InitWeightBias(layers[index].Count, layers[index].Depth);
}
#endregion
#region 准备训练对象
LOptimizer optimizer = new LAdam(0.1);
ThreeLayerNet net = null;
bool isRead = false;
string path = @"d:\wen\good\";
for (int i = 0; i < 50000; i++)
{
Debug.Write("开始时间" + DateTime.Now.ToString("HH:mm:ss\n"));
var inputFeature = neuralnetworkforward();
if (net == null)
{
net = new ThreeLayerNet(inputFeature.GetLength(1), 200, 2, isRead: isRead);
}
net.Input = inputFeature;
double loss = net.forward(lable);
Debug.Write(string.Format("{0}:{1}当前误差：{2}\n", DateTime.Now.ToString("HH:mm:ss"), i, loss));
double accuracy = net.Accuracy(lable);
Debug.Write(string.Format("{0}:{1}当前识别精度：{2}\n", DateTime.Now.ToString("HH:mm:ss"), i, accuracy));
LMatrix dout = net.backward(lable);
List <LMatrix> grads = net.Gradient();
List <LMatrix> param = net.GetParams();
string strWB = JsonConvert.SerializeObject(param.Select(x => x.Matrix));
if (!Directory.Exists(path + i.ToString()))
{
Directory.CreateDirectory(path + i.ToString());
}
File.WriteAllText(path + i.ToString() + @"\wb_" + accuracy.ToString() + "_.txt", strWB);
var _backdout = dout.ResetSize(38, CurRow, CurColumn);
neuralnetworkbackward(_backdout);
#region 卷积梯度更新
int infolen = LayerInfo.Length;
for (int idx = 0; idx < infolen; idx++)
{
LayerInfo[idx].UpdateGradient(param, grads);
}
#endregion
optimizer.Update(param, grads);
//isRead = true;
//记录每次的卷积值信息
File.WriteAllText(path + i.ToString() + @"\LayerInfo.txt", JsonConvert.SerializeObject(LayerInfo));
File.WriteAllText(path + i.ToString() + @"\ConvInfo.txt", JsonConvert.SerializeObject(ConvLayer));
}
#endregion
}