BP Neural Network Implementation for Test Data Prediction

BP neural network is a classic artificial neural network model widely applied in various data prediction scenarios. In test data prediction tasks, the BP neural network learns feature patterns from training data to achieve accurate predictions on test datasets. The implementation typically involves defining network architecture using frameworks like TensorFlow or PyTorch, where the model structure is initialized with specified layers and activation functions.

The first critical step in implementing test data prediction is data preprocessing. Normalizing both training and test datasets is essential to eliminate scale differences between features, ensuring all features reside in similar numerical ranges. This significantly enhances neural network training efficiency and prediction accuracy. In code implementation, this can be achieved using sklearn's StandardScaler or MinMaxScaler classes, which standardize data distribution before feeding into the network.

Constructing a BP neural network requires careful design of network architecture. This includes determining the number of nodes in the input layer (matching feature dimensions), hidden layers, and output layer (typically corresponding to prediction targets). The hidden layer design must balance model complexity and generalization capability. Code implementation often involves configuring layers through sequential models, where Dense layers with ReLU activation handle hidden layers while output layers use linear or sigmoid activation depending on regression/classification tasks.

During training, the network continuously adjusts weight parameters through backpropagation algorithms to minimize prediction errors. The training process implements gradient descent optimization (e.g., Adam optimizer) with loss functions like Mean Squared Error. After training completion, the saved network parameters form the prediction model. When applying the trained BP neural network to predict compressive strength for 23 test samples, the network generates predictions based on learned feature-strength mapping relationships through forward propagation computations.

The final output typically includes numerical predictions and visualizations. Plotting prediction result graphs enables intuitive comparison between predicted and actual values, facilitating model performance evaluation. Code implementation often utilizes matplotlib or seaborn for visualization, while performance metrics like RMSE or R² are calculated using sklearn.metrics. Analyzing these results helps assess the BP neural network's performance in current prediction tasks and guides subsequent model optimization through techniques like hyperparameter tuning or cross-validation.