/// <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); } }