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Bayesian Neural Net
Bayesian Neural Networks: 2 Fully Connected in TensorFlow and Pytorch
This chapter continues the series on Bayesian deep learning. The previous chapter is available here and the next chapter here. In this chapter we’ll explore alternative solutions to conventional dense neural networks. These alternatives will invoke probability distributions over each weight in the neural network resulting in a single model that effectively contains an infinite ensemble of neural networks trained on the same data. We’ll use this knowledge to solve an important problem of our age: how long to boil an egg.
Chapter Objectives:
- Become familiar with variational inference with dense Bayesian models
- Learn how to convert a normal fully connected (dense) neural network to a Bayesian neural network
- Appreciate the advantages and shortcomings of the current implementation
The data is from an experiment in egg boiling. The boil durations are provided along with the egg’s weight in grams and the finding on cutting it open. Findings are categorised into one of three classes: under cooked, soft-boiled and hard-boiled. We want the egg’s outcome from its weight and boiling time. The problem is insanely simple, so much so that the data is near being linearly…
