Source Code for BP Neural Network Training - Algorithm Implementation Section

The core of the BP neural network algorithm lies in adjusting network weights through error backpropagation, with the training process consisting of two key phases: forward propagation and backpropagation. The algorithm implementation typically includes the following core steps: In the forward propagation phase, the computation progresses from the input layer to the hidden layer. Each hidden layer neuron performs a weighted sum of input signals and generates output through an activation function (such as Sigmoid or ReLU). In code implementation, this involves matrix multiplication between input vectors and weight matrices, followed by element-wise activation function application. The hidden layer outputs continue propagating to the output layer, undergoing similar weighted summation and activation function processing to ultimately produce the network's prediction results. The backpropagation phase begins at the output layer by calculating error terms using the partial derivatives of the loss function (such as mean squared error) with respect to the prediction results. The algorithm then backpropagates error signals layer by layer, computing each neuron's contribution to the total error. For hidden layer error calculation, the implementation must consider the weighted sum of errors from all neurons in the subsequent layer, requiring careful matrix operations and derivative calculations. Weight updates follow gradient descent principles, adjusting connection weights between layers based on error signals and learning rate. The weight update from input to hidden layer requires computing the product of hidden layer error terms, learning rate, and input signals. In code, this typically involves storing intermediate values during forward pass for efficient backward computation. This process iterates until network convergence, with practical implementations requiring appropriate stopping conditions such as maximum iteration count or error threshold. Implementation considerations include selecting the optimal number of hidden nodes, setting appropriate learning rates, and accurately computing activation function derivatives. These factors critically influence network training effectiveness. Manual implementation of these algorithm steps provides deeper understanding of BP neural network mechanics, with proper code organization separating forward propagation, error calculation, and weight update routines for maintainability and performance optimization.