I study Bayesian Deep learning and for implement it, i found a library tensor-flow probability , for Dense layer in standard deep learning , there Dense-Variational ,Dense-reparameterize and Dense-flipout , what is difference between them? and is there any specification for them to implement ie: for example if i have classification problem which to implement , or it's no difference to implement none of them?
Let's go through the layers one by one.
When you use a DenseVariational layer in TensorFlow Probability, you're telling the model to start with a general assumption about its weights, then update this assumption based on the data it sees, and try to make these updates as accurate as possible. It is useful when you want to estimate uncertainty in your model predictions, as it provides a distribution over the weights rather than point estimates. It's particularly helpful in scenarios where data is scarce or noisy.
The DenseReparameterization Layer modifies the sampling process so that it's differentiable, allowing for backpropagation. In short, it separates the stochastic part of the layer from the deterministic part. It is a good choice when you need gradients to flow through the sampling process. It's typically used when you want to perform more stable and efficient training of Bayesian neural networks.
The DenseFlipout Layer decorrelates the gradients within a mini-batch by applying random sign flips during training. This results in more efficient and stable training. It is particularly useful in scenarios where you're dealing with large datasets and need to ensure efficient training with lower variance in the gradients. It's often chosen for problems where computational efficiency and training stability are crucial.
Regarding your example of a classification problem, the choice depends on your specific needs: