You may find np.einsum() more intuitive than np.dot() for aligning axes -- it's certainly more explicit.
On Fri, Apr 19, 2019 at 3:59 PM C W <tmrs...@gmail.com> wrote: > 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 >> > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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