  In this technical report we study the problem of propagation of uncertainty
(in terms of variances of given uni-variate normal random variables) through
typical building blocks of a Convolutional Neural Network (CNN). These include
layers that perform linear operations, such as 2D convolutions,
fully-connected, and average pooling layers, as well as layers that act
non-linearly on their input, such as the Rectified Linear Unit (ReLU). Finally,
we discuss the sigmoid function, for which we give approximations of its first-
and second-order moments, as well as the binary cross-entropy loss function,
for which we approximate its expected value under normal random inputs.
