Bayesian neural network for classification


I have a (beginners) question about using Bayesian neural networks for classification. In the tutorial on Bayesian neural networks on the website, the output of the neural network is fed into a Gaussian random variable like this:

y = Normal(loc=neural_network(x),scale=tf.ones(K) * 0.1)

and subsequently

inference = ed.KLqp({W_0: qW_0, b_0: qb_0,
                     W_1: qW_1, b_1: qb_1}, data={y: y_train})

For my particular example, I want the network to classify images from 10 different categories. I tried making a categorical distribution like this:

y = Categorical(logits=neural_network(x),dtype=tf.int64)

However, when I feed that into the feed_dict in the inference step, I get an error because the shapes don’t match up. My variable y now has shape (1,) while the y_train data have shape (10,) (A one out of 10 representation). This makes me wonder what the correct way is in Edward of using a Bayesian neural network for classification.


The Categorical distribution outputs only the label of the category with the highest probability. Either you must change your y_train data to the index of the correct label (for example for the MNIST data set just change it to the digit in the image) or you can use the OneHotCategorical distribution. The OneHotCategorical distiribution is the same as the Categorical distribution only that it takes the one hot vector as in input and outputs the probabilities for each label.


Thanks! That is running now. Could I also ask you how in such a case we would evaluate how well the model does in Edward? Like, get a score on a test data set.


Did you get this working? I am also a beginner and trying to figure out how to use the learned model to make perform classification on new data.


here is a classification example, classifying among two Gaussians