我在Java中编写了一个简单的人工神经网络作为项目的一部分。当我开始训练数据时（使用我收集的训练集）每个时期的错误计数很快稳定（达到约30％的准确度）然后停止。在测试ANN时，任何给定输入的所有输出都完全相同。
我正在尝试输出一个介于0和1之间的数字（0表示将股票归类为跌幅，1表示将冒险者分类 - 0.4-0.6表示稳定性）
在RapidMiner Studios中添加相同的训练数据时，会创建一个具有更高（70 +％）精度的正确ANN，因此我知道数据集很好。人工智能逻辑中肯定存在一些问题。
以下是运行和调整权重的代码。任何和所有帮助表示赞赏！
public double[] Run(double[] inputs) {
//INPUTS
for (int i = 0; i < inputNeurons.length; i++) {
inputNeurons[i] = inputs[i];
}
for (int i = 0; i < hiddenNeurons.length; i++) {
hiddenNeurons[i] = 0;
} //RESET THE HIDDEN NEURONS
for (int e = 0; e < inputNeurons.length; e++) {
for (int i = 0; i < hiddenNeurons.length; i++) {
//Looping through each input neuron connected to each hidden neuron
hiddenNeurons[i] += inputNeurons[e] * inputWeights[(e * hiddenNeurons.length) + i];
//Summation (with the adding of neurons) - Done by taking the sum of each (input * connection weight)
//The more weighting a neuron has the more "important" it is in decision making
}
}
for (int j = 0; j < hiddenNeurons.length; j++) {
hiddenNeurons[j] = 1 / (1 + Math.exp(-hiddenNeurons[j]));
//sigmoid function transforms the output into a real number between 0 and 1
}
//HIDDEN
for (int i = 0; i < outputNeurons.length; i++) {
outputNeurons[i] = 0;
} //RESET THE OUTPUT NEURONS
for (int e = 0; e < hiddenNeurons.length; e++) {
for (int i = 0; i < outputNeurons.length; i++) {
//Looping through each hidden neuron connected to each output neuron
outputNeurons[i] += hiddenNeurons[e] * hiddenWeights[(e * outputNeurons.length) + i];
//Summation (with the adding of neurons) as above
}
}
for (int j = 0; j < outputNeurons.length; j++) {
outputNeurons[j] = 1 / (1 + Math.exp(-outputNeurons[j])); //sigmoid function as above
}
double[] outputs = new double[outputNeurons.length];
for (int j = 0; j < outputNeurons.length; j++) {
//Places all output neuron values into an array
outputs[j] = outputNeurons[j];
}
return outputs;
}
public double[] CalculateErrors(double[] targetValues) {
//Compares the given values to the actual values
for (int k = 0; k < outputErrors.length; k++) {
outputErrors[k] = targetValues[k] - outputNeurons[k];
}
return outputErrors;
}
public void tuneWeights() //Back Propagation
{
// Start from the end - From output to hidden
for (int p = 0; p < this.hiddenNeurons.length; p++) //For all Hidden Neurons
{
for (int q = 0; q < this.outputNeurons.length; q++) //For all Output Neurons
{
double delta = this.outputNeurons[q] * (1 - this.outputNeurons[q]) * this.outputErrors[q];
//DELTA is the error for the output neuron q
this.hiddenWeights[(p * outputNeurons.length) + q] += this.learningRate * delta * this.hiddenNeurons[p];
/*Adjust the particular weight relative to the error
*If the error is large, the weighting will be decreased
*If the error is small, the weighting will be increased
*/
}
}
// From hidden to inps -- Same as above
for (int i = 0; i < this.inputNeurons.length; i++) //For all Input Neurons
{
for (int j = 0; j < this.hiddenNeurons.length; j++) //For all Hidden Neurons
{
double delta = this.hiddenNeurons[j] * (1 - this.hiddenNeurons[j]);
double x = 0; //We do not have output errors here so we must use extra data from Output Neurons
for (int k = 0; k < this.outputNeurons.length; k++) {
double outputDelta = this.outputNeurons[k] * (1 - this.outputNeurons[k]) * this.outputErrors[k];
//We calculate the output delta again
x = x + outputDelta * this.hiddenWeights[(j * outputNeurons.length) + k];
//We then calculate the error based on the hidden weights (x is used to add the error values of all weights)
delta = delta * x;
}
this.inputWeights[(i * hiddenNeurons.length) + j] += this.learningRate * delta * this.inputNeurons[i];
//Adjust weight like above
}
}
}
答案 0 :(得分：2)
经过长时间的报道后，我认为您可以通过以下几点找到答案：