Convolutional Neural Network (CNN) Algorithm for Deep Learning Implementation

This program implements the Convolutional Neural Network (CNN) algorithm for deep learning, with distinct functionalities divided between training and testing procedures. The training program consists of multiple sequential steps including data preprocessing, network architecture definition, model training, and model evaluation. The data preprocessing phase involves data loading routines and data augmentation techniques to enhance dataset diversity. Network architecture definition comprises the construction of convolutional layers (using Conv2D operations), pooling layers (typically max-pooling implementations), and fully connected layers (dense layers with activation functions). Model training incorporates loss function definition (commonly cross-entropy for classification tasks) and parameter optimization through gradient descent algorithms like Adam or SGD. Model evaluation calculates performance metrics including accuracy, recall, and precision using validation datasets. The testing program is designed for predicting classification results on new data samples. It includes data preprocessing, model loading, and model inference stages. Data preprocessing involves data loading scripts and normalization procedures to ensure input consistency. Model loading handles both the neural network architecture reconstruction and trained parameter restoration from saved checkpoints. Model inference implements forward propagation through the network, involving data input processing and output result decoding (typically using softmax activation for classification probabilities). Through utilizing this program, users can learn fundamental principles of deep learning algorithms, master CNN construction and training methodologies, and develop practical skills for applying deep learning solutions to real-world problems. The code structure demonstrates key implementation aspects such as layer configuration, backpropagation mechanics, and efficient data pipeline management.