What is the method for executing max/mean pooling on a 2-dimensional array with the use of numpy?

edit

The method for executing max/mean pooling on a 2-dimensional array using numpy is:

1. Import the numpy library:

import numpy as np

1. Create a 2-dimensional array:

array = np.array([[1, 2, 3, 4],
                  [5, 6, 7, 8],
                  [9, 10, 11, 12],
                  [13, 14, 15, 16]])

1. Define the pooling parameters:

pool_size = (2, 2) # Pooling window size
stride = 2 # Stride length

1. Execute max/mean pooling:

max_pooled = np.max(pooling_array(array, pool_size, stride), axis=(2, 3))
mean_pooled = np.mean(pooling_array(array, pool_size, stride), axis=(2, 3))

Note: pooling_array is a function that returns a padded and reshaped array with the same dimensions as array and is not built-in with numpy. It can be defined as follows:

def pooling_array(array, pool_size, stride):
    # Pad array with zeros to handle border conditions
    array = np.pad(array, ((0, 0), (0, 1), (0, 1)), mode='constant')
    # Reshape array to handle window sizes
    shape = ((array.shape[0] - pool_size[0]) // stride + 1,
             (array.shape[1] - pool_size[1]) // stride + 1,
             pool_size[0], pool_size[1])
    strides = (array.strides[0] * stride, array.strides[1] * stride,
               array.strides[0], array.strides[1])
    return np.lib.stride_tricks.as_strided(array, shape=shape, strides=strides)