@Maverick06, The main use of pooling layer is to reduce the number of features. In average pooling we will only do for small blocks of the input. But in global average pooling we will do for the whole input.
For a single image, without considering the batch size let’s consider an input of dimension 4 (height) x 4 (width) x 4 (channels), if you apply global average pooling it will take the average value for the whole 4 (height) x 4 (width). It will take average for all 16 values and it will be done for all 4 channels seperately. The result will be 1 x 4 (channels), if you need to need to preserve dimension, you can add keep_dims as true and you will get (1 x 1 x 4). Now you can understand why it’s called global. It is mainly used to replace fully connected layers in CNNs as it looks more like that.
In average pooling, you will not take for whole height and width dimension, you will do for small blocks in it by mentioning the stride and pool_size. So if you mention stride as 2 and poolsize as 2 x 2 and if you apply for this example you will get 2(pooled_height) x 2(pooled_width) x 4(channels) as output.