LeNet-5 Training on MNIST Dataset with Enhanced Implementation

This resource utilizes an implementation of the LeNet-5 network architecture for processing the MNIST dataset. The code implementation incorporates references from UFLDL tutorials and R. B. Palm's CNN implementation. To accommodate the dataset specifications, the input dimensions of LeNet-5 were modified to 28×28 pixels, and full connectivity was established between each feature map in the C3 layer and every feature map in the S4 layer. The training process achieved a final accuracy of 99.1% through several enhancements: The training dataset underwent comprehensive data augmentation including rotation, translation, scaling, and flipping operations to increase dataset diversity and volume. Dropout regularization was implemented to prevent overfitting by randomly disabling neurons during training. The Adam optimizer was employed for efficient convergence acceleration, while cross-entropy served as the loss function for supervised learning. Key implementation details include: - Custom layer configuration handling through dimension adjustment functions - Data preprocessing pipelines for real-time augmentation during training batches - Dynamic learning rate scheduling integrated with Adam optimization - Gradient computation and backpropagation mechanisms adapted for the modified architecture Through iterative hyperparameter tuning and architectural refinements, this implementation delivers an efficient model suitable for digit classification tasks.