Once you understand how convolutional layers work, the pooling layers are quite easy to grasp. A pooling layer typically works on every input channel independently, so the output depth is the same as the input depth. You may alternatively pool over the depth dimension, as we will see next, in which case the image's spatial dimensions (height and width) remain unchanged, but the number of channels is reduced. Let's see a formal definition of pooling layers from a well-known TensorFlow website:
"The pooling ops sweep a rectangular window over the input tensor, computing a reduction operation for each window (average, max, or max with argmax). Each pooling op uses rectangular windows of size called ksize ...
