public string TrainModel(IpfsJob job)
{
var tmpInput = Ipfs.Get <JToken>(job.input);
var tmpTarget = Ipfs.Get <JToken>(job.target);
var seq = CreateSequential(job.Model);
var inputData = tmpInput.SelectToken("data").ToObject <float[]>();
var inputShape = tmpInput.SelectToken("shape").ToObject <int[]>();
var inputTensor = controller.floatTensorFactory.Create(_data: inputData, _shape: inputShape, _autograd: true);
var targetData = tmpTarget.SelectToken("data").ToObject <float[]>();
var targetShape = tmpTarget.SelectToken("shape").ToObject <int[]>();
var targetTensor = controller.floatTensorFactory.Create(_data: targetData, _shape: targetShape, _autograd: true);
var grad = controller.floatTensorFactory.Create(_data: new float[] { 1, 1, 1, 1 },
_shape: new int[] { 4, 1 });
Loss loss;
switch (job.config.criterion)
{
case "mseloss":
loss = new MSELoss(this.controller);
break;
case "categorical_crossentropy":
loss = new CategoricalCrossEntropyLoss(this.controller);
break;
case "cross_entropy_loss":
loss = new CrossEntropyLoss(this.controller, 1); // TODO -- real value
break;
case "nll_loss":
loss = new NLLLoss(this.controller);
break;
default:
loss = new MSELoss(this.controller);
break;
}
var optimizer = new SGD(this.controller, seq.getParameters(), job.config.lr, 0, 0);
for (var i = 0; i < job.config.iters; ++i)
{
var pred = seq.Forward(inputTensor);
var l = loss.Forward(pred, targetTensor);
l.Backward();
// TODO -- better batch size
optimizer.Step(100, i);
}
var resultJob = new Ipfs();
var response = resultJob.Write(new IpfsJob(job.input, job.target, seq.GetConfig(), job.config));
return(response.Hash);
}
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);
}
static void Main(string[] args)
{
string[] files = System.IO.Directory.GetFiles("res");
files = files.OrderBy(p => p).ToArray();
float[][][,] datax = new float[1][][, ];
float[][][,] datah = new float[1][][, ];
float[][][,] datac = new float[1][][, ];
float[][][,] datay = new float[1][][, ];
datax[0] = new float[10][, ];
datah[0] = new float[10][, ];
datac[0] = new float[10][, ];
datay[0] = new float[10][, ];
MSELoss mloss = new MSELoss();
ConvLSTM convLSTM = new ConvLSTM(10, 10, 5);
for (int r = 0; r < files.Length - 10; r++)
{
for (int t = 0; t < 10; t++)
{
string file = files[r + t];
float[,] anno1 = DenseCRF.util.readRADARMatrix(file);
datax[0][t] = anno1;
datah[0][t] = new float[anno1.GetLength(0), anno1.GetLength(1)];
datac[0][t] = new float[anno1.GetLength(0), anno1.GetLength(1)];
file = files[r + t + 10];
datay[0][t] = DenseCRF.util.readRADARMatrix(file);
}
var star = DateTime.Now;
var(h_next, c_next) = convLSTM.Forward(datax, datah, datac);
var loss = mloss.Forward(h_next, datay);
Console.WriteLine("误差:" + loss);
var grid = mloss.Backward();
var grid2 = convLSTM.backward(grid);
convLSTM.update();
var end = DateTime.Now;
Console.WriteLine((end - star).TotalMilliseconds);
}
}
/// <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]);
}
static void 单层()
{
// float[][][] prev_state = new float[2][][];
LSTMCELL lstm1 = new LSTMCELL(7, 1);
LSTMCELL lstm2 = new LSTMCELL(10, 1);
//var x_numpy = JsonConvert.DeserializeObject<float[][]>(util.getstr("D:\\x_numpy.json"));
//var h_numpy = JsonConvert.DeserializeObject<float[][]>(util.getstr("D:\\h_numpy.json"));
//var c_numpy = JsonConvert.DeserializeObject<float[][]>(util.getstr("D:\\c_numpy.json"));
//var dh_numpy = JsonConvert.DeserializeObject<float[][]>(util.getstr("D:\\dh_numpy.json"));
var x_numpy = new float[1][];
var h_numpy = new float[1][];
var c_numpy = new float[1][];
var h_numpy2 = new float[1][];
var c_numpy2 = new float[1][];
var dh_numpy = new float[1][];
var dataall = getdata();
for (int i = 0; i < 30000; i++)
{
for (int j = 0; j < 1; j++)
{
h_numpy[j] = new float[1];
h_numpy2[j] = new float[1];
}
for (int j = 0; j < 1; j++)
{
c_numpy[j] = new float[1];
c_numpy2[j] = new float[1];
}
var loss = 0.0f;
MSELoss mloss = new MSELoss();
for (int j = 0; j < 10; j++)
{
x_numpy[0] = dataall[j + i];
var dhgird = lstm1.Forward(x_numpy, h_numpy, c_numpy);
h_numpy = dhgird.Item1;
c_numpy = dhgird.Item2;
dh_numpy[0] = new float[] { dataall[i + j + 1][0] };
loss += mloss.Forward(h_numpy, dh_numpy);
var gird = mloss.Backward();
lstm1.backward(gird);
lstm1.update();
}
Console.WriteLine("误差:" + loss);
}
for (int i = 30000; i < 40000; i++)
{
for (int j = 0; j < 1; j++)
{
h_numpy[j] = new float[1];
}
for (int j = 0; j < 1; j++)
{
c_numpy[j] = new float[1];
}
var loss = 0.0f;
MSELoss mloss = new MSELoss();
for (int j = 0; j < 10; j++)
{
x_numpy[0] = dataall[j + i];
dh_numpy[0] = new float[] { dataall[j + i + 1][0] };
var dhgird = lstm1.Forward(x_numpy, h_numpy, c_numpy);
h_numpy = dhgird.Item1;
c_numpy = dhgird.Item2;
//fff += c_numpy[0][0];
//var gird = mloss.Backward();
//lstm.backward(gird);
}
loss += mloss.Forward(h_numpy, dh_numpy);
Console.WriteLine("误差:" + loss + ",预测：" + (h_numpy[0][0]) * 1000 + ",期望:" + (dh_numpy[0][0]) * 1000);
}
}
static void 单层()
{
// float[][][] prev_state = new float[2][][];
GRU gru = new GRU(7, 15, 1);
var x_numpy = new float[1][];
var h_numpy = new float[1][];
var h_numpy2 = new float[1][];
var dh_numpy = new float[1][];
var dataall = getdata();
for (int i = 0; i < 35000; i++)
{
for (int j = 0; j < 1; j++)
{
h_numpy[j] = new float[15];
h_numpy2[j] = new float[15];
}
var loss = 0.0f;
MSELoss mloss = new MSELoss();
for (int j = 0; j < 10; j++)
{
x_numpy[0] = dataall[j + i];
var dhgird = gru.Forward(x_numpy, h_numpy);
h_numpy = dhgird.Item2;
// c_numpy = dhgird.Item2;
dh_numpy[0] = new float[] { dataall[i + j + 1][0] };
loss += mloss.Forward(dhgird.Item1, dh_numpy);
var gird = mloss.Backward();
gru.backward(gird);
gru.update();
}
Console.WriteLine("误差:" + loss);
}
for (int i = 35000; i < 40000; i++)
{
for (int j = 0; j < 1; j++)
{
h_numpy[j] = new float[15];
}
var loss = 0.0f;
MSELoss mloss = new MSELoss();
dynamic DY = null;
for (int j = 0; j < 10; j++)
{
x_numpy[0] = dataall[j + i];
dh_numpy[0] = new float[] { dataall[j + i + 1][0] };
var dhgird = gru.Forward(x_numpy, h_numpy);
h_numpy = dhgird.Item2;
DY = dhgird.Item1;
}
loss += mloss.Forward(DY, dh_numpy);
Console.WriteLine("误差:" + loss + ",预测：" + (DY[0][0]) * 1000 + ",期望:" + (dh_numpy[0][0]) * 1000);
}
}
