CNN Building Blocks

Convolution layer

A neural network wouldn’t be named convolutional if it didn’t have at least one convolution layer. A two-dimensional convolution layer scans the input tensor with a small two-dimensional tensor (the convolution kernel) that has the same number of channels as the input tensor. For each pixel in the input tensor, a scalar product gets computed between the neighborhood around the central pixel and the convolution kernel. The result is a tensor that has a high activation where the input tensor looks like the convolution kernel.

To illustrate the functioning of a convolution layer, let’s design one by hand.

Consider the following image: