Thanks, you are right. I overlooked it's for addition. The original problem was that I have matrix X (RBG image, 3 layers), and vector y.
I wanted to do np(X, y.T). >>> X.shape # 100 of 28 x 28 matrix (100, 28, 28) >>> y.shape # Just one 28 x 28 matrix (1, 28, 28) But, np.dot() gives me four axis shown below, >>> z = np.dot(X, y.T) >>> z.shape (100, 28, 28, 1) The fourth axis is unexpected. Should y.shape be (28, 28), not (1, 28, 28)? Thanks again! On Fri, Apr 19, 2019 at 6:39 PM Andras Deak <deak.and...@gmail.com> wrote: > On Sat, Apr 20, 2019 at 12:24 AM C W <tmrs...@gmail.com> wrote: > > > > Am I miss reading something? Thank you in advance! > > Hey, > > You are missing that the broadcasting rules typically apply to > arithmetic operations and methods that are specified explicitly to > broadcast. There is no mention of broadcasting in the docs of np.dot > [1], and its behaviour is a bit more complicated. > Specifically for multidimensional arrays (which you have), the doc says > > If a is an N-D array and b is an M-D array (where M>=2), it is a sum > product over the last axis of a and the second-to-last axis of b: > dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m]) > > So your (3,4,5) @ (3,5) would want to collapse the 4-length axis of > `a` with the 3-length axis of `b`; this won't work. If you want > elementwise multiplication according to the broadcasting rules, just > use `a * b`: > > >>> a = np.arange(3*4*5).reshape(3,4,5) > ... b = np.arange(4*5).reshape(4,5) > ... (a * b).shape > (3, 4, 5) > > > [1]: https://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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