2d convolution using numpy

conv2d.py

'''
Created on Jul 13, 2015

@author: kashefy
'''
import numpy as np
from scipy import signal

if __name__ == '__main__':
    
    x = np.array([[1, 1, 1, 0, 0],
                  [0, 1, 1, 1, 0],
                  [0, 0, 1, 1, 1],
                  [0, 0, 1, 1, 0],
                  [0, 1, 1, 0, 0]],
                 dtype='float')
    
    w_k = np.array([[1, 0, 1],
                    [0, 1, 0],
                    [1, 0, 1],],
                   dtype='float')
    
    w_k = np.rot90(w_k, 2)
    
    print x.shape, w_k.shape
    f = signal.convolve2d(x, w_k, 'valid')
    
    print f
    
    weights = np.random.randn()
    
    pass

Hello random person, I am random person from the interwebs. This gist was the second result on Google for 'numpy 2D convolution' for me. I have a random person request; can you retitle your gist "2D Convolution with Scipy"?
Most people have numpy installed with python, but scipy is more specialised and requires deliberate installation.

Agreed, I landed here hoping for numpy, not scipy.

I haven't evaluated, but you might consider this https://stackoverflow.com/a/42579291/2184122

Hey guys, I'm also trying to implement the convolution of arbitrary shaped ndarrays in NumPy here you may wanna take a look.

I feel this is a much-optimized approach to the problem https://stackoverflow.com/questions/43086557/convolve2d-just-by-using-numpy
def filter(im, fil):
# Get the shape of the 4d array
view_shape = tuple(np.subtract(im.shape, fil.shape) + 1) + fil.shape
strd = np.lib.stride_tricks.as_strided
# Get the new view of the array as required
subM = strd(im, shape = view_shape, strides = im.strides * 2)
# for every i,j element in filter multiply with 2d array ( of subM
# and return thier sum
return np.einsum('ij,ijkl->kl',fil,subM.T).T

filter(x, np.rot90(w_k,2)