In this talk, I will mainly focus on inferring the uncertainty information when using a deep neural network in TensorFlow. In particular, regression tasks, which have been less focussed compared to classification problems, will be mainly considered. First, a mixture density network will be implemented with TensorFlow where its superiority will be shown compared to ordinary regression networks. Then, two different methods, epistemic uncertainty and the entropy of a Gaussian mixture model, will be presented to estimate the uncertainty information along with the prediction output.
