# Convolutional Neural Network with constant kernels

I'm starting to learn about CNNs, and I have this question that I haven't been able to answer. Sorry if it is too basic.

I know that in a CNN, the network learns to extract relevant features of images in order to classify them. My question is: if I just update the weights of the fully-connected layer and leave the kernels constant, will the network learn how to classify as well?

(The kernels might be a Gaussian blur or a sharpenning, or whatever comes to mind).

• Why don't you try it out and see? Jul 7, 2017 at 17:36