Python simple Convolution in numpy

Question

I have written this simple convolution function in numpy. But the final array values are all still zero. Please help me correct this function.

def convolve(a_prev, w, b):
    pad = 0
    stride = 1
    s1 = a_prev.shape
    s2 = w.shape
    f = s2[1]
    m = s1[0]
    n_c = s2[0]
    n_h = int((s1[1] - f + 2 * pad) / stride) + 1
    n_w = int((s1[2] - f + 2 * pad) / stride) + 1
    a = np.zeros((m,n_h,n_w,n_c), dtype=np.float32)

    for n in range(m):
        for z in range(n_c):
            y = 0
            x = 0
            while ((y+f) <= n_h):
                # Edit: forget to inialize the x = 0
                while ((x+f) <= n_w):  
                    #a[n,y,x,z] = np.sum(a_prev[n,y:y+f,x:x+f]*w[z]) + b[z,0]
                    a[n,y,x,z] = np.sum(np.multiply(a_prev[n,y:y+f,x:x+f],w[z])) + b[z,0]
                    x += stride
                y += stride
    
    print(a[0,85,:,3])
    return a

shape of a_prev is [num_exmamples,height, width, 3] and w is [num_filters,3,3,3]

I found the reason, why it was not working, i make a programming error and forget to initialize the x = 0 before while loop. Its working fine now. Below is the correct function.

def convolve(a_prev, kernel, b, pad = 0, stride = 1):
    m = a_prev.shape[0]
    prev_h = a_prev.shape[1]
    prev_w = a_prev.shape[2]
    f = kernel.shape[1]
    n_c = kernel.shape[0]
    new_h = int((prev_h - f + 2 * pad) / stride) + 1
    new_w = int((prev_w - f + 2 * pad) / stride) + 1
    az = np.zeros((m,new_h,new_w,n_c), dtype=np.float32)

    for n in range(m):
        for z in range(n_c):
            y = 0
            while (y+f) <= prev_h:
                x = 0
                while (x+f) <= prev_w:
                    az[n,y,x,z] = np.sum(a_prev[n,y:y+f,x:x+f]*kernel[z]) + b[z,0]
                    x += stride
                y += stride
    
    return az

something you expect like from convolution function of libraries like pytorch or tensorflow. — Vishwas Bhaskar
– Vishwas Bhaskar, Commented Jan 29, 2021 at 22:10
i mean the main question is, that values are being assigned to array 'a' but when I print some part of that array in the end it's all zeros. — Vishwas Bhaskar
– Vishwas Bhaskar, Commented Jan 29, 2021 at 22:16

dee_eeb · Accepted Answer · 2021-01-29 18:41:28Z

2

There is an easier way to do convolution without using the previous input as a function input.

import numpy as np

def convolution(x, h):
# x and h are numpy arrays
M, N = np.size(x), np.size(h)
y = np.zeros(M+N-1)
# Initialise y with the length of the output signal
for m in np.arange(M):
    for n in np.arange(N):
        y[m+n] += x[m]*h[n]
return y

This function uses the basic definition of convolution for discrete signals

answered Jan 29, 2021 at 18:41

dee_eeb

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1 Comment

Vishwas Bhaskar Over a year ago

Thanks for answering, but I am looking for convolution in neural networks over volumes.

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