Lets say we have a 3 Channel (RGB) image given by some matrix A A = [[[198 218 227] [196 216 225] [196 214 224] … … [185 201 217] [176 192 208] [162 178 194]] and a blur kernal as K = [[0.1111, 0.1111, 0.1111], [0.1111, 0.1111, 0.1111], [0.1111, 0.1111, 0.1111]] #which is actually … Read more
Python 3.5 has an explicit operator @ for the dot product, so you can write a = A @ B instead of a = numpy.dot(A,B)
The idea with tensordot is pretty simple – We input the arrays and the respective axes along which the sum-reductions are intended. The axes that take part in sum-reduction are removed in the output and all of the remaining axes from the input arrays are spread-out as different axes in the output keeping the order … Read more
Straightforward way to do that is: import numpy as np a=np.array([[1,2,3],[3,4,5]]) b=np.array([[1,2,3],[1,2,3]]) np.sum(a*b, axis=1) which avoids the python loop and is faster in cases like: def npsumdot(x, y): return np.sum(x*y, axis=1) def loopdot(x, y): result = np.empty((x.shape[0])) for i in range(x.shape[0]): result[i] = np.dot(x[i], y[i]) return result timeit npsumdot(np.random.rand(500000,50),np.random.rand(500000,50)) # 1 loops, best of 3: … Read more