How can you implement contrastive divergence in training a restricted Boltzmann machine RBM for generative modeling

Question

Can you tell me How i can implement contrastive divergence in training a restricted Boltzmann machine (RBM) for generative modeling?

Answer 1

To implement Contrastive Divergence (CD) for training a Restricted Boltzmann Machine (RBM), you perform a forward pass to compute probabilities, a Gibbs sampling step to reconstruct visible data, and calculate the weight updates based on the difference in statistics between the data and reconstruction. You can refer to the following code:

In the above code, we are using the following:

• Positive Phase: Compute statistics using the original data.
• Negative Phase: Use Gibbs sampling to generate reconstructions and compute statistics.
• Weight Update: Adjust weights and biases based on the difference between the two phases.
• Hyperparameters: kkk determines the number of Gibbs sampling steps. Commonly, k=1k=1k=1 (CD-1) is used for efficiency.

Hence by referring to the following  you can  implement contrastive divergence in training a restricted Boltzmann machine RBM for generative modeling