我正试图用这个https://www.youtube.com/watch?v=KkwX7FkLfug教程编写一个使用反向传播的神经网络。
我试着用c来做，并且改变了一些细节。
他和我的主要区别是我不知道从一开始每个节点上有多少个神经元。下面是我的api在代码中的工作方式。
class main
{
static void Main(string[] args)
{
List<bool> testData = new List<bool>() {true};
System.Console.WriteLine("In MAIN! \n");
int inputNeurons = 64;
int outputNeurons = 2;
double eta = 0.1; // 0.0..1.0 training rate
double alpha = 0.5; // 0.0..n momentum
NeuralNet neuralNet = new NeuralNet(eta, alpha);
// order matters, first layer is for input, last layer is for output
neuralNet.AddLayer(inputNeurons); // input layer
// multiple hidden layers could go here
neuralNet.AddLayer(outputNeurons); // output Layer
neuralNet.MakeConnections();
neuralNet.Train(testData);
neuralNet.GetResults();
Console.ReadLine();
}
}
下面是我为NeuralNet编写的其余代码，其中一些是伪代码，或者只是在我尝试连接工作时打印语句。
namespace NeuralNetJO
{
class Program
{
static void Main(string[] args)
{
}
}
public class NeuralNet
{
public NeuralNet(double newEta, double newAlpha)
{
numLayers = -1; // numLayers tracks how many layers of neurons the neural net has
eta = newEta;
alpha = newAlpha;
}
/**
* GetResults()
* returns array of results and weights
*/
public void GetResults()
{
foreach (Layer l in n_layer)
{
foreach (Neuron n in l.n_neurons)
{
}
}
}
public void MakeConnections()
{
// For each layer
if (numLayers > 0)
{
for (int i = 0; i < n_layer.Count; i++)
{
for (int j = 0; j < n_layer[i].n_neurons.Count; j++)
{
//For each Node in Layer that isn't final layer, connect it to every node in the next layer
}
}
}
}
public void FeedForward(List<int> inputVals)
{
}
public void BackProp(List<int> targetVals)
{
}
public void AddLayer(int numNeurons)
{
numLayers++;
if (numLayers > 0) //If first layer
{
Layer layer = new Layer(numNeurons, numLayers, n_layer[numLayers - 1]);
n_layer.Add(layer);
}
else
{
Layer layer = new Layer(numNeurons, numLayers);
n_layer.Add(layer);
}
}
public void Train(List<bool> testData)
{
Console.WriteLine("Training...");
if (testData[0] == false)
{
Console.WriteLine("\t False");
}
else
{
Console.WriteLine("\t True");
}
}
//-------------- Member Variables --------------//
private List<Layer> n_layer = new List<Layer>(); // List of layers, layers are comprised of Neurons
private int numLayers;
double eta;
double alpha;
}
public class Layer
{
// mumLayer is for debug purposes only
public Layer(int numNeurons, int numLayer, Layer prevLayer = null)
{
myLayer = numLayer;
for (int i = 0; i <= numNeurons; ++i) // Add a bias Neuron
{
System.Console.Write(i + ": "); // Show line number for accurate Neuron count
Neuron neuron = new Neuron(i);
Console.WriteLine(" in layer #" + numLayer);
n_neurons.Add(neuron);
}
if (prevLayer != null)
{
foreach (Neuron n in prevLayer)
{
}
}
}
public List<Neuron> n_neurons = new List<Neuron>();
int myLayer;
}
/**
* Neuron is a class that holds public information about Neurons
* This include weights, value, input and output locations.
*/
public class Neuron
{
public Neuron(int index) // Constructor
{
myIndex = index;
System.Console.Write("Creating Neuron " + myIndex);
}
private double transferFunction(double x)
{
return x;
}
private double transferFunctionDerivative(double x)
{
return x;
}
double randomWeight()
{
// set weights random
Random r = new Random(0);
return r.NextDouble() * 2 - 1.0;
}
public double Value { get; set; } // Output value
List<Connection> outPutWeights; // Fpr each connection for the layer to the right
public int numOutputs { set; get; } // This will be set when numLayers > 0;
int myIndex;
double eta; // training rate
double alpha; // momentum
double gradient;
private double sumDOW(Layer nextLayer)
{
return 1;
}
}
public class Connection
{
public double Weight { get; set; }
public double DeltaWeight { get; set; }
}
}
在教程中，他给每个神经元下一层的神经元数量在我的代码中，我不能这样做，因为我一次添加一个层添加的第一层是输入层，第二层到n-1层是隐藏层，最后一层是输出层。
我很难用一个好的算法来包装我的大脑，这个算法可以循环遍历第一层的每个神经元，然后在第二层将它们链接到每个神经元，以此类推。我假设它需要递归并以某种方式使用我的“numlayers”变量。
正如您在我的使用代码中看到的，我调用了
neuralNet.MakeConnections()，我愿意在添加其他层时添加连接；如果有人能看到这样做的好地方的话。这是我最初的想法，但当我陷入困境时，我给自己画了一张图，并决定在这个函数中做它可能会更简单。
提前感谢您的帮助！
最佳答案
回复：I am assuming it will need to be recursive
一点也不。也不需要额外的变量numLayers，您已经知道使用n_layers.Count添加了多少层。
// in class NeuralNet
public void MakeConnections()
{
// start at input layer, stop at last hidden layer
for(int i=0; i<(n_layer.Count()-1); ++i)
{
Layer thisLayer = n_layer[i]; // only for typing convenience
Layer nextLayer = n_layer[i+1];
for(int n1=0; n1<thisLayer.n_neurons.Count(); ++n1)
{
Neuron thisNeuron = thisLayer.n_neurons[n1];
for(int n2=0; n2<nextLayer.n_neurons.Count(); ++n2)
{
thisNeuron.outPutWeights.Add(
new Connection(randomWeight(),randomWeight());
}
}
}
}
如果您希望在添加层时添加这些连接，则它可能如下所示：
// in class NeuralNet
public void AddLayer(int numNeurons)
{
Layer layer = new Layer(numNeurons,
n_layers.Count(),
(n_layers.Count > 0)
? n_layer[n_layers.Count-1]
: null);
n_layers.Add(layer);
}
和
// in class Layer
public Layer(int numNeurons,
int numLayer,
Layer prevLayer = null)
{
for (int i = 0; i <= numNeurons; ++i)
{
Neuron neuron = new Neuron(i);
n_neurons.Add( neuron );
}
if (prevLayer != null)
{
for(int i=0; i<prevLayer.n_neurons.Count(); ++i)
{
for(int j=0; j<n_neurons.Count(); ++j)
{
prevLayer.n_neurons[i].outputWeights.Add(
new Connection(randomWeight(), randomWeight() ) );
}
}
}
}
当然，没有必要把这两种风格联系起来那样你就会有双重关系。要么用
MakeConnections一次全部完成，要么在飞行中完成。关于c# - 用于将一层中的所有节点连接到下一层中的所有节点的算法，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/38929881/