/// <summary>
/// 一个受保护的帮助函数，用于训练单个学习样本
/// </summary>
/// <param name="trainingSample">
/// 使用的训练样本
/// </param>
/// <param name="currentIteration">
/// 当前训练时期（假设为正且小于<c> trainingEpochs </ c>）
/// </param>
/// <param name="trainingEpochs">
/// 训练时期数（假定为正）
/// </param>
protected override void LearnSample(TrainingSample trainingSample, int currentIteration, int trainingEpochs)
{
// 这里没有验证
int layerCount = layers.Count;
// 设置输入向量
inputLayer.SetInput(trainingSample.InputVector);
for (int i = 0; i < layerCount; i++)
{
layers[i].Run();
}
// 设置错误
meanSquaredError += (outputLayer as ActivationLayer).SetErrors(trainingSample.OutputVector);
// 反向传播错误
for (int i = layerCount; i > 0;)
{
ActivationLayer layer = layers[--i] as ActivationLayer;
if (layer != null)
{
layer.EvaluateErrors();
}
}
// 优化突触权重和神经元偏差值
for (int i = 0; i < layerCount; i++)
{
layers[i].Learn(currentIteration, trainingEpochs);
}
}
/// <summary>
/// A protected helper function used to train single learning sample
/// </summary>
/// <param name="trainingSample">
/// Training sample to use
/// </param>
/// <param name="currentIteration">
/// Current training epoch (Assumed to be positive and less than <c>trainingEpochs</c>)
/// </param>
/// <param name="trainingEpochs">
/// Number of training epochs (Assumed to be positive)
/// </param>
protected override void LearnSample(TrainingSample trainingSample, int currentIteration, int trainingEpochs)
{
// No validation here
int layerCount = layers.Count;
// Set input vector
inputLayer.SetInput(trainingSample.InputVector);
for (int i = 0; i < layerCount; i++)
{
layers[i].Run();
}
// Set Errors
meanSquaredError += (outputLayer as ActivationLayer).SetErrors(trainingSample.OutputVector);
// Backpropagate errors
for (int i = layerCount; i > 0;)
{
ActivationLayer layer = layers[--i] as ActivationLayer;
if (layer != null)
{
layer.EvaluateErrors();
}
}
// Optimize synapse weights and neuron bias values
for (int i = 0; i < layerCount; i++)
{
layers[i].Learn(currentIteration, trainingEpochs);
}
}
/// <summary>
/// 创建一个新的激活神经元
/// </summary>
/// <param name="parent">
/// 包含此神经元的父层
/// </param>
/// <exception cref="System.ArgumentNullException">
/// 如果<c>父</ c>是<c> null </ c>
/// </exception>
public ActivationNeuron(ActivationLayer parent)
{
Helper.ValidateNotNull(parent, "parent");
this.input = 0d;
this.output = 0d;
this.error = 0d;
this.bias = 0d;
this.parent = parent;
}
/// <summary>
/// 创建新的反向传播网络，具有指定的输入和输出层。 （在使用构造函数之前，您需要使用适当的突触来连接所有层。对网络结构创建后所做的任何更改都可能导致完全故障）
/// </summary>
/// <param name="inputLayer">
/// 输入层
/// </param>
/// <param name="outputLayer">
/// 输出层
/// </param>
/// <exception cref="ArgumentNullException">
/// 如果<c> inputLayer </ c>或<c> outputLayer </ c>为<c> null </ c>
/// </exception>
public BackpropagationNetwork(ActivationLayer inputLayer, ActivationLayer outputLayer)
: base(inputLayer, outputLayer, TrainingMethod.Supervised)
{
this.meanSquaredError = 0d;
this.isValidMSE = false;
}
