Thanks but the following is taken from the paper in section 5:
“In existing literature, however, dropout is used in CNNs only after inner-product layers – equivalent
to approximately integrating these alone. Here we wish to integrate over the kernels of the CNN as
well. Thus implementing a Bayesian CNN we apply dropout after all convolution layers as well as
inner-product layers.”
As stated, integration is done over the kernel of the CNN. Do I have the right idea? If so, how would I go about implementing?