我在训练我的nn时遇到了一些困难。当我使用 10 个训练集时，在训练过程结束时，神经网络只针对最后两个进行训练。我输入的值与我用来训练网络的值相同，但我得到了错误的结果，除了最后两个。在我看来，新的 nn 记忆抑制了旧记忆。我使用了 64 个输入神经元，隐藏层中的 42 个神经元和一个输出神经元。Sigmoid 函数用于激活神经元。训练输入和预期输出在 0 到 1 的范围内。有谁知道可能导致问题的原因？
Neuron b = new Neuron();
Fft f = new Fft();
float e = 2.71828f;
float eta = 0.05f;
float alpha = 0.05f;
float[] saw = new float[42];
float[] dh = new float[42];
float error = 0;
float dto = 0;
Random broj = new Random();
TextReader br = new StreamReader("d:/trening.txt");
TextReader ir = new StreamReader("d:\\input.txt");
float NextFloat(Random rng, float min, float max)
{
return (float)(min + (rng.NextDouble() * (max - min)));
}
public void load()//load memory
{
int i, j;
byte[] floatBytes;
BinaryReader br = new BinaryReader(File.Open("d:/memorija.txt", FileMode.Open));
for (j = 0; j <= 41; j++)
{
for (i = 0; i <= 64; i++)
{
floatBytes = br.ReadBytes(4);
b.w12[i][j] = BitConverter.ToSingle(floatBytes, 0);
}
}
for (j = 0; j <= 1; j++)
{
for (i = 0; i <= 41; i++)
{
floatBytes = br.ReadBytes(4);
b.w23[i][j] = BitConverter.ToSingle(floatBytes, 0);
}
}
br.Close();
}
public void trening()//Get training inputs and expected outputs
{ //Calls process methode
int i, n,ct=0;
using (TextReader tr = new StreamReader("d:/trening.txt"))
{
do
{
ct++;
} while (tr.ReadLine() != null);
tr.Close();
}
for (n = 0; n < (ct-1)/65; n++)
{
for (i = 1; i <= 65; i++)
b.input[i] = Convert.ToSingle(br.ReadLine());
process(b.input[65]);
target.Text = ((b.input[65]).ToString());
}
}
public void process(double t)//Trains nn using backpropagation
{
error = 0;
do
{
int i, j, k;
BinaryWriter bw = new BinaryWriter(File.Open("d:\\memorija.txt", FileMode.Create));
i = k = j = 0;
for (j = 1; j <= 41; j++)
{
b.ulaz2[j] = b.w12[0][j];
for (i = 1; i <= 64; i++)
{
b.ulaz2[j] += b.input[i] * b.w12[i][j];
} b.izlaz2[j] = (float)(1.0 / (1.0 + Math.Pow(e, -b.ulaz2[j])));
if (b.izlaz2[j] < 0)
MessageBox.Show(b.izlaz2[j].ToString());
}
for (k = 1; k <= 1; k++)
{
b.ulaz3 = b.w23[0][k];
for (j = 1; j <= 41; j++)
{
b.ulaz3 += b.izlaz2[j] * b.w23[j][k];
} b.izlaz = (float)(1.0 / (1.0 + Math.Pow(e, -b.ulaz3)));
error += (float)(0.5 * (t - b.izlaz) * (t - b.izlaz));
dto = (float)(t - b.izlaz) * b.izlaz * (1 - b.izlaz);
}
for (j = 1; j <= 41; j++)
{
saw[j] = 0;
for (k = 1; k <= 1; k++)
{
saw[j] += dto * b.izlaz2[j];
} dh[j] = saw[j] * b.izlaz2[j] * (1 - b.izlaz2[j]);
}
for (j = 1; j <= 41; j++)
{
b.w12d[0][j] = eta * dh[j] + alpha * b.w12d[0][j];
b.w12[0][j] += b.w12d[0][j];
for (i = 1; i <= 64; i++)
{
b.w12d[i][j] = eta * b.input[i] * dh[j] + alpha * b.w12d[i][j];
b.w12[i][j] += b.w12d[i][j];
}
}
for (k = 1; k <= 1; k++)
{
b.w23d[0][k] = eta * dto + alpha * b.w23d[0][k];
b.w23[0][k] += b.w23d[0][k];
for (j = 1; j <= 41; j++)
{
b.w23d[j][k] = eta * b.izlaz2[j] * dto + alpha * b.w23d[j][k];
b.w23[j][k] += b.w23d[j][k];
}
}
for (j = 0; j <= 41; j++)
{
for (i = 0; i <= 64; i++)
bw.Write(b.w12[i][j]);
}
for (j = 0; j <= 1; j++)
{
for (i = 0; i <= 41; i++)
bw.Write(b.w23[i][j]);
}
bw.Close();
izlazb.Text = Convert.ToString(b.izlaz);
errorl.Text = Convert.ToString(Math.Abs(b.izlaz - b.input[64]));
} while (Math.Abs(b.izlaz - t) > 0.03);
}
public void test()//This methode gets input values and gives output based on previous training
{
int i = 0, j = 0, k = 0;
for (i = 1; i < 65; i++)
b.input[i] = (float)Convert.ToDouble(ir.ReadLine());
for (j = 1; j <= 41; j++)
{
b.ulaz2[j] = b.w12[0][j];
for (i = 1; i <= 64; i++)
{
b.ulaz2[j] += b.input[i] * b.w12[i][j];
} b.izlaz2[j] = (float)(1.0 / (1.0 + Math.Pow(e, -b.ulaz2[j])));
}
for (k = 1; k <= 1; k++)
{
b.ulaz3 = b.w23[0][k];
for (j = 1; j <= 41; j++)
{
b.ulaz3 += b.izlaz2[j] * b.w23[j][k];
} b.izlaz = (float)(1.0 / (1.0 + Math.Pow(e, -b.ulaz3)));
} izlazb.Text = Convert.ToString(b.izlaz);
target.Text = "/";
errorl.Text = "/";
}
public void reset()//Resets memory
{
BinaryWriter fw = new BinaryWriter(File.Open("d:\\memorija.txt", FileMode.Create));
int i = 0;
int j = 0;
Random broj = new Random();
for (j = 0; j <= 41; j++)
{
for (i = 0; i <= 64; i++)
{
b.w12[i][j] = 0;
b.w12[i][j] = 2 * (NextFloat(broj, -0.5f, 0.5f));
fw.Write(b.w12[i][j]);
}
}
for (j = 0; j <= 1; j++)
{
for (i = 0; i <= 41; i++)
{
b.w23[i][j] = 0;
b.w23[i][j] = 2 * (NextFloat(broj, -0.5f, 0.5f));
fw.Write(b.w23[i][j]);
}
}
fw.Close();
}
}
}
和神经元类
public class Neuron
{
public float[][] w12 = new float[65][];//(65, 42);
public float[][] w12d = new float[65][];//(65, 42);
public float[][] w23 = new float[42][];//(42,2);
public float[][] w23d = new float[42][];//(42, 2);
public float[] ulaz2 = new float[42];
public float[] izlaz2 = new float[42];
public float ulaz3;
public float[] input =new float[66];
public static float[] ioutput;
public float izlaz;
public void arrayInit()
{
int i, j;
for (i = 0; i <=64; i++)
{
w12[i] = new float[42];
w12d[i] = new float[42];
}
for (i = 0; i <42; i++)
{
w23[i] = new float[2];
w23d[i] = new float[2];
}
for (j = 0; j < 42; j++)
for (i = 0; i <=64; i++)
{
w12[i][j] = 0;
w12d[i][j] = 0;
}
for (j = 0; j < 2; j++)
for (i = 0; i < 42; i++)
{
w23[i][j] = 0;
w23d[i][j] = 0;
}
}
}