Convolutional Neural Network

Definition

A convolutional neural network (CNN) is a regularized type of fully connected Neural Network that learns features by itself via filter optimization. It consists of convolution layers.

Convolutional Layer

The layer’s parameters consist of a set of learnable filters that slide over the input image. Each filter performs a Convolution operation, computing the Dot Product between the filter values and the input values at each position. The output of the convolution operation is a feature map.

The output size is determined by the input size, filter size, padding, and stride. $\text{output size}=\frac{N-F+2P}{S}+1$ where $N$ is the input size, $F$ is the filter size, $P$ is padding, and $S$ is the stride.

Stride

Stride refers to the step size the convolution filter moves each time it slides over the input.

Padding

Padding adds extra border pixels around the input images. It preserves spatial dimension of the feature map, and retains information at the borders.