Question

我在Java中实现了一个简单的神经网络，用于解决XOR问题（http://www.mind.ilstu.edu/curriculum/artificial_neural_net/xor_problem_and_solution.php）。

我想知道你需要什么神经元和一个NeuronLayer等课程，所以我决定尝试只使用一个课程。

不幸的是，在一些时期之后，它达到了一个错误是两个值之一的状态：0.5或-0.5。

我尝试过限制选择起始重量但不会改变任何内容的范围。

也许，你们中的一些人可以帮助我发现错误或改善网络。提前谢谢！

这是我的代码：

int inCount = 2;
int hidCount = 3;
int outCount = 1;

float learningRate = 0.01;

float[] inputs = new float[inCount];
float[] hidden = new float[hidCount];
float[] outputs = new float[outCount];

float[][] IHweights = new float[inCount][hidCount];
float[][] HOweights = new float[hidCount][outCount];

void setup ()
{
  for (int i = 0; i < IHweights.length; i++)
  {
    for (int e = 0; e < IHweights[i].length; e++)
    {
      float newWeight = random(-1,1);
      while(newWeight > -0.5 && newWeight < 0.5)
      {
        newWeight = random(-1,1);
      }
      IHweights[i][e] = newWeight;
      println(IHweights[i][e]+"\n");
    }
  }

  for (int i = 0; i < HOweights.length; i++)
  {
    for (int e = 0; e < HOweights[i].length; e++)
    {
      float newWeight = random(-1,1);
      while(newWeight > -0.5 && newWeight < 0.5)
      {
        newWeight = random(-1,1);
      }
      HOweights[i][e] = newWeight;
    }
  }
}

void draw ()
{
  float[] inData = {round(random(1)),round(random(1))};
  println(inData[0]+"   "+inData[1]);
  float[] expResult = {(int) inData[0]^(int) inData[1]};
  println("  ->  "+expResult[0]);
  feedForward(inData,expResult);
}

public float sigmoid (float x)
{
  if (x>10)
  {
    return 1;
  }
  if (x<-10)
  {
    return 0;
  }
  return 1/(1+exp(-x));
}

public void feedForward (float[] input, float[] expOut)
{
  inputs = input;

  for (int i = 0; i < hidCount; i++)
  {
    float var = 0;
    for (int e = 0; e < inCount; e++)
    {
      var += inputs[e] * IHweights[e][i];
    }
    hidden[i] = sigmoid(var);
  }

  for (int i = 0; i < outCount; i++)
  {
    float var = 0;
    for (int e = 0; e < hidCount; e++)
    {
      var += hidden[e] * HOweights[e][i];
    }
    outputs[i] = sigmoid(var);
  }

  float[] error = new float[outCount];
  float[] deltaOut = new float[outCount];

  for (int i = 0; i < outCount; i++)
  {
    error[i] = expOut[i] - outputs[i];
    deltaOut[i] = outputs[i] * (1-outputs[i]) * error[i];
  }

  float[] deltaHid = new float[hidCount];

  for (int i = 0; i < hidCount; i++)
  {
    deltaHid[i] = hidden[i] * (1-hidden[i]);
    for (int e = 0; e < outCount; e++)
    {
      deltaHid[i] += HOweights[i][e] * outputs[e];
    }
  }

  for (int i = 0; i < inCount; i++)
  {
    for (int e = 0; e < hidCount; e++)
    {
      IHweights[i][e] += inputs[i] * deltaHid[e] * learningRate;
    }
  }

  for (int i = 0; i < hidCount; i++)
  {
    for (int e = 0; e < outCount; e++)
    {
      HOweights[i][e] += hidden[i] * deltaOut[e] * learningRate;
    }
  }

  println(error[0]);
}

只使用一个班级

0 个答案: