Deep Belief Network (DBN) Implementation Code

MATLAB 14.04M 242 views 0 downloads 1 credits
Tags:

Resource Overview

This is a test program implementing a Deep Belief Network (DBN) algorithm using stacked Restricted Boltzmann Machines (RBMs). The current implementation employs contrastive divergence for training individual RBMs and includes both pre-training and fine-tuning phases. However, the performance results have been suboptimal, possibly due to parameter tuning challenges or architectural limitations in the neural network layers.

Detailed Documentation

This test program implements a Deep Belief Network (DBN) algorithm architecture consisting of multiple hidden layers trained using greedy layer-wise pre-training. The implementation features unsupervised learning through Restricted Boltzmann Machines (RBMs) for feature extraction, followed by supervised fine-tuning using backpropagation. Despite implementing standard DBN components including sigmoid activation functions and contrastive divergence training, the current results demonstrate suboptimal performance in pattern recognition tasks. We invite researchers and developers with expertise in deep learning architectures to share alternative DBN implementations, optimized hyperparameter configurations, or improved training methodologies. Collaborative knowledge sharing will advance our collective understanding of deep neural networks and enhance algorithmic performance through community-driven improvements. Thank you for your contributions to this learning initiative.

You May Also Like