public dynamic Forward(float[][] input, float[][] h_prev, float[][] c_prev)
{
//a_vector = np.dot(x, self.weight_ih.T) + np.dot(h_prev, self.weight_hh.T)
//a_vector += self.bias_ih + self.bias_hh
Xinput = input;
xh_prev = h_prev;
xc_prev = c_prev;
var ih = convLayerih.Forward(input);
var hh = convLayerhh.Forward(h_prev);
var a_vector = Matrix.MatrixAdd(ih, hh);
List <float[][]> liast = Matrix.chunk(a_vector, 4, 1);
var a_i = liast[0];
var a_f = liast[1];
var a_c = liast[2];
var a_o = liast[3];
input_gate = input_gate_s.Forward(a_i);
forget_gate = forget_gate_s.Forward(a_f);
cell_memory = cell_memory_tl.Forward(a_c);
output_gate = output_gate_s.Forward(a_o);
var c_next_temp = c_next_mul.Forward(forget_gate, c_prev);
var mulin_gate = mulin_gate_mul.Forward(input_gate, cell_memory);
var c_next = Matrix.MatrixAdd(c_next_temp, mulin_gate);
var h_next = h_next_mul.Forward(output_gate, cell_tl.Forward(c_next));
// dh_prev = Matrix.zroe(h_next.Length, h_next[0].Length);
return(h_next, c_next);//上次的状态，上次的记忆
}
//MulLayer ZHZTL = new MulLayer();
public (dynamic, dynamic) Forward(float[][] input, float[][] state)
{
var H = state;
var X = convLayerih.Forward(input);
var HH = convLayerhh.Forward(H);
List <float[][]> listX = Matrix.chunk(X, 3, 1);
List <float[][]> listHH = Matrix.chunk(HH, 3, 1);
var ZS = Matrix.MatrixAdd(listX[0], listHH[0]);
var Z = ZSL.Forward(ZS);
var RS = Matrix.MatrixAdd(listX[1], listHH[1]);
var R = RSL.Forward(RS);
var RH = RHM.Forward(R, listHH[2]);
var RHS = Matrix.MatrixAdd(listX[2], RH);
var H_tilda = THL.Forward(RHS);
var ZH = ZHL.Forward(Z, H);
var Z1 = Matrix.MatrixSub(1, Z);
var ZHT = ZHTL.Forward(Z1, H_tilda);
H = Matrix.MatrixAdd(ZH, ZHT);
var Y = convLayerhq.Forward(H);
return(Y, H);
}
public void ForwardBackwardTest()
{
var input = new TensorOld(new double[]
{
4, 6, 1, 4,
8, 4, 5, 1,
5, 3, 5, 7,
1, 7, 2, 8,
}, 1, 1, 4, 4);
var conv = new ConvLayer(4, 2, 1, 1);
conv.SetFilters(new TensorOld(new double[]
{
1, 1, 0, 0,
0, 0, 1, 1,
1, 0, 1, 0,
0, 1, 0, 1,
}, 4, 1, 2, 2));
var expected = new TensorOld(new double[]
{
0, 0, 0, 0, 0,
4, 10, 7, 5, 4,
8, 12, 9, 6, 1,
5, 8, 8, 12, 7,
1, 8, 9, 10, 8,
4, 10, 7, 5, 4,
8, 12, 9, 6, 1,
5, 8, 8, 12, 7,
1, 8, 9, 10, 8,
0, 0, 0, 0, 0,
0, 4, 6, 1, 4,
0, 12, 10, 6, 5,
0, 13, 7, 10, 8,
0, 6, 10, 7, 15,
0, 1, 7, 2, 8,
4, 6, 1, 4, 0,
12, 10, 6, 5, 0,
13, 7, 10, 8, 0,
6, 10, 7, 15, 0,
1, 7, 2, 8, 0,
}, 1, 4, 5, 5);
conv.PrepareTrain(input);
var acutal = conv.Forward(input);
Assert.Equal(acutal, expected);
var error = expected / 10;
var back = conv.Backward(error);
}
/// <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]);
}
