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问题描述
这是我用于检查convolve2d的正确性的代码
Here is my code which I used for checking the correctness of convolve2d
import numpy as np
from scipy.signal import convolve2d
X = np.random.randint(5, size=(10,10))
K = np.random.randint(5, size=(3,3))
print "Input's top-left corner:"
print X[:3,:3]
print 'Kernel:'
print K
print 'Hardcording the calculation of a valid convolution (top-left)'
print (X[:3,:3]*K)
print 'Sums to'
print (X[:3,:3]*K).sum()
print 'However the top-left value of the convolve2d result'
Y = convolve2d(X, K, 'valid')
print Y[0,0]
在我的电脑上,结果如下:
On my computer this results in the following:
Input's top-left (3x3) corner:
[[0 0 0]
[1 1 2]
[1 3 0]]
Kernel:
[[4 1 1]
[0 3 3]
[2 1 2]]
Hardcording the calculation of a valid convolution (top-left)
[[0 0 0]
[0 3 6]
[2 3 0]]
Sums to
14
However the top-left value of the convolve2d result
10
背景故事:我一直在调试一个convnet库,不知何故渐变总是错误的。几周后,我得出结论,一切都应该正常,所以我用裸手检查了convolve2d函数。
Background story: I've been debugging a convnet library, and somehow the gradients were always wrong. After a few weeks I concluded that everything should be working fine, so I checked the convolve2d function by bare hand.
推荐答案
表达式(X [:3,:3] * K).sum()
不正确。为了卷积,你必须颠倒内核,例如(X [:3,:3] * K [:: - 1,:: - 1])sum()
The expression (X[:3,:3]*K).sum()
is not correct. For convolution, you have to reverse the kernel, e.g. (X[:3,:3]*K[::-1,::-1]).sum()
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