Three-Layer Data Training Program for BP Artificial Neural Network

The Backpropagation Neural Network (BPNN) is a classic supervised learning model whose core mechanism involves adjusting network weights through the backpropagation algorithm. The standard three-layer architecture consists of an input layer, hidden layer, and output layer. Below is an enhanced explanation of the training workflow and key implementation logic:

Data Preparation Phase Input data requires normalization preprocessing to prevent features with different scales from interfering with weight updates. For classification tasks, the output layer typically uses One-Hot encoding. Before training, the dataset should be partitioned into training, validation, and test sets. In code implementation, this involves using sklearn's StandardScaler for normalization and train_test_split for dataset splitting.

Forward Propagation Process The input layer receives feature vectors, which undergo nonlinear transformation through activation functions (e.g., Sigmoid or ReLU) in the hidden layer before propagating to the output layer. The output layer generates predictions via Softmax (multi-class) or Sigmoid (binary classification) and calculates the loss (e.g., cross-entropy or mean squared error) against true labels. Code implementation typically involves matrix multiplication operations between layers and activation function applications.

Backpropagation and Weight Update Based on the gradient of the loss function, error contributions are calculated layer by layer backward from the output layer. The chain rule is applied to compute partial derivatives for weights and biases in each layer, with parameters adjusted through gradient descent (or optimizers like Adam) combined with a learning rate. To address vanishing gradient issues in hidden layers, common strategies include Xavier/Glorot weight initialization and batch normalization techniques.

Iteration and Termination Conditions Training iterations are controlled by epochs, with Early Stopping implemented to prevent overfitting. After each iteration, model performance is evaluated on the validation set. Training terminates when loss converges or accuracy meets predefined thresholds. In practice, this requires implementing callback functions to monitor validation metrics.

Extended Considerations The number of hidden layer nodes can be determined experimentally - too many may cause overfitting while too few leads to underfitting. Dynamic learning rate adjustment strategies (e.g., cosine annealing) can improve convergence efficiency. Introducing Dropout layers enhances model generalization capability by randomly disabling neurons during training.