See the following illustration: 3x3 window, 2x2 stride pooling on a 7x7 image

Max kernel: IR + KR - 1 rows, IC + KC - 1 columnsĪ pooling layer is defined by window width and height, and the horizontal and vertical stride size (how many units to move at once in either direction).

Mid kernel: IR rows, IC columns error if either KR or KC is even.Min kernel: IR - KR + 1 rows, IC - KC + 1 columns invalid if the resulting rows or columns are zero or negative.If the input image has IR rows and IC columns, and the kernel has KR rows and KC columns, the output dimensions are defined as follows: One pixel per positioning of the kernel is produced, resulting in a 2D array which can be smaller than (min), equal to (mid), or larger than (max) the input image. For a mid kernel, the center of the kernel is placed over each pixel of the image for a max kernel, all positions where any pixel overlaps with the kernel is considered. A min kernel extracts values at positions where the entire kernel overlaps with the original image.

It is defined by kernel width and height, and kernel mode (min, mid, or max). Convolutional layerĪ convolutional layer works like a kernel in image processing. This challenge is about Convolutional neural networks, and its two main building blocks, namely Convolutional layer and Pooling layer.įor simplicity, we ignore the 'depth' of the images and intermediate tensors, and just look at the width and height.

Height and width layer list stack