Performing single prediction

[aronayne]

Hi @yaringal

Thanks for sharing this information.

The code you posted on your blog (http://mlg.eng.cam.ac.uk/yarin/blog_3d801aa532c1ce.html#uncertainty-sense) is to be utilized as part of training, therefore T = the number of network training epochs ?

probs = []
for _ in xrange(T):
    probs += [model.output_probs(input_x)]
predictive_mean = numpy.mean(prob, axis=0)
predictive_variance = numpy.var(prob, axis=0)
tau = l**2 * (1 - model.p) / (2 * N * model.weight_decay)
predictive_variance += tau**-1

As 1 forward pass is utilized to make a prediction p on a trained network, when performing the uncertainty calculation of p does T become 1 so then

for _ in xrange(T):
    probs += [model.output_probs(input_x)]

is just :

probs += [model.output_probs(input_x)] ?