static void test2D()
{
float[][][,] x = JsonConvert.DeserializeObject <float[][][, ]>(getstr("D:\\x.json"));
float[][][,] y = JsonConvert.DeserializeObject <float[][][, ]>(getstr("D:\\y.json"));
float[][][,] w = JsonConvert.DeserializeObject <float[][][, ]>(getstr("D:\\w1.json"));
float[] wb = JsonConvert.DeserializeObject <float[]>(getstr("D:\\w2.json"));
Conv2DLayer cl = new Conv2DLayer(1, 1, 3, 1, 2, false);
MSELoss mloss = new MSELoss();
cl.weights = new float[w.GetLength(0)][][, ];
for (int a = 0; a < w.GetLength(0); a++)
{
cl.weights[a] = new float[w[0].GetLength(0)][, ];
for (int b = 0; b < w[a].GetLength(0); b++)
{
cl.weights[a][b] = new float[0, 0];
cl.weights[a][b] = w[a][b];
}
}
cl.basicData = wb;
//向前传播
dynamic temp = cl.Forward(x);
SigmodLayer sl = new SigmodLayer();
temp = sl.Forward(temp);
//TanhLayer tl = new TanhLayer();
//temp = tl.forward(temp);
//MulLayer ml = new MulLayer();
//temp = ml.forward(temp, y);
float loss = mloss.Forward(temp, y);
//向后传播
dynamic grad = mloss.Backward();//计算误差梯度
grad = sl.Backward(grad);
//grad = ml.backward(grad);
//grad = tl.backward(grad);
dynamic grad3 = cl.backward(grad);//卷积计算在 所有计算的最后面进行
prirt(grad3.grid);
prirt(grad3.basic);
}
/// <summary>
/// BP网络测试
/// </summary>
static void BP()
{
float[][] x = JsonConvert.DeserializeObject <float[][]>(util.getstr("D:\\bpx.json")); //训练数据
float[][] y = JsonConvert.DeserializeObject <float[][]>(util.getstr("D:\\bpy.json")); //训练标签
float[][] w1 = JsonConvert.DeserializeObject <float[][]>(util.getstr("D:\\bpw.json"));
ConvLayer cl1 = new ConvLayer(13, 5, true);
cl1.weights = w1;
SigmodLayer sl = new SigmodLayer();
float lr = 0.5f;
ConvLayer cl2 = new ConvLayer(5, 1, true);
//SigmodLayer s2 = new SigmodLayer();
int i = 0, a = 0;
while (a < 5000)
{
//i = 0;
//while (i < 100)
//{
// float[][] xx2 = new float[1][];
// xx2[0] = new float[x[0].GetLength(0)];
// for (var f = 0; f < x[0].GetLength(0); f++)
// {
// xx2[0][f] = x[i][f];
// }
dynamic ff = cl1.Forward(x);
ff = sl.Forward(ff);
ff = cl2.Forward(ff);
// dynamic ff22 = s2.forward(ff);
//计算误差
MSELoss mloss = new MSELoss();
//float[][] yy2= new float[1][];
//yy2[0] = y[i];
var loss = mloss.Forward(ff, y);
Console.WriteLine("误差:" + loss);
dynamic grid = mloss.Backward();
//反传播w2
// dynamic grid2 =s2.backward(grid);
dynamic w22 = cl2.backweight(grid);
//反传播W1
dynamic grid1 = cl2.backward(grid);
grid1 = sl.Backward(grid1);
dynamic w11 = cl1.backweight(grid1);
cl2.weights = Matrix.MatrixSub(cl2.weights, Matrix.multiply(w22.grid, lr));
cl2.basicData = Matrix.MatrixSub(cl2.basicData, Matrix.multiply(w22.basic, lr));
cl1.weights = Matrix.MatrixSub(cl1.weights, Matrix.multiply(w11.grid, lr));
cl1.basicData = Matrix.MatrixSub(cl1.basicData, Matrix.multiply(w11.basic, lr));
i++;
// }
a++;
}
//测试网络
float[][] xx = new float[1][];
xx[0] = new float[x[0].GetLength(0)];
var aa = 3;
for (var f = 0; f < x[0].GetLength(0); f++)
{
xx[0][f] = x[aa][f];
}
dynamic ff2 = cl1.Forward(xx);
ff2 = sl.Forward(ff2);
ff2 = cl2.Forward(ff2);
util.prirt(ff2);
util.prirt(y[aa]);
}
