Figure 5 illustrates a pooling layer function 500 with a 2x2 receptive field and a stride of two. The pooling layer function 500 is an example of the maxpool pooling technique. The outputs of all the neurons in a particular receptive field of the input layer 502 are replaced by the maximum valued one of those outputs in the pooling layer 504. Other options for pooling layers are average pooling and L2-norm pooling. The reason to use a pooling layer is that once a specific feature is recognized in the original input volume (there will be a high activation value), its exact location is not as important as its relative location to the other features. Pooling layers can drastically reduce the spatial dimension of the input layer 502 from that pont forward in the neural network (the length and the width change but not the depth). This serves two main purposes. The first is that the amount of parameters or weights is greatly reduced thus lessening the computation cost. The second is that it will control overfitting. Overfitting refers to when a model is so tuned to the training examples that it is not able to generalize well when applied to live data sets.