In this recipe, we will learn about two key configuration hyperparameters of CNN, which are strides and padding. Strides are used mainly to reduce the size of the output volume. Padding is another technique that lets us preserve the dimensions of the input volume in the output volume, thus enabling us to extract the low-level features efficiently.
Strides: Stride, in very simple terms, means the step of the convolution operation. Stride specifies the amount by which filters convolve around the input. For example, if we specify the value of stride argument as 1, that means the filter will shift one unit at a time over the input matrix.
Strides can be used for multiple purposes, primarily the following:
- To avoid feature overlapping
- To achieve smaller spatial dimensionality of the output volume
In the following diagram, you...
