# Convolutional-like operations

Convolutional-like operations move a sliding window over an input tensor of size (N, Cin, Hin, Win), where $$N$$ is the batch size, $$C$$ the number of channels and $$H, W$$ the width and height of the input. In each sliding window, we apply the same operation e.g. linear transformation conv2d, or channel-wise max max pooling. The output of a Convolutional-like operation is a tensor of size (N, Cout, Hout, Wout).

All convolutional-like operations share a set of common hyper-parameters:

• The size of the sliding window $$K$$
• The spacing between sliding windows, known as stride
• The position of the top-right window, as controlled by padding.

# Striding

Striding is a parameter used in both the conv2d layers and the pooling layers. It controls how far the sliding window jumps between consecutive outputs. Striding naturally down-samples the output tensor, by skipping certain input locations. Increase striding when you want a smaller overlap overlap between neighboring windows and smaller output sizes. Typically, the striding of height and width dimensions are set to be equal $$kH = kW$$.