Understanding CNNs: Fully Connected Layers

Fully connected layers

Fully connected layers are a fully connected set of weights from the input to the output. These fully connected weights are able to learn global information as they are connected from each input to each output. Also, having such layers of full connectedness allows us to combine features learned by the convolution layers preceding the fully connected layers globally to produce meaningful outputs.

Let’s define the output of the last convolution or pooling layer to be of size $p\times o \times d$, where $p$ is the height of the input, ...