On Sun, 31 May 2020 at 03:42, Giuseppe G. A. Celano <[email protected]> wrote: > > I am trying to use very small matrices. Is there any way to calculate the > partial derivatives of "loss2" below? > > import numpy as np > from sympy import * > > n, d, n2, d2 = 5, 7, 4, 3 > > x = np.random.randn(n, d) > y = np.random.randn(n, d2) > > w1 = MatrixSymbol("l", 7, 4) > w1 = Matrix(w1) > > w2 = MatrixSymbol("p", 4, 3) > w2 = Matrix(w2) > > h2 = x * w1 > predicted = h2 * w2 > > loss2 = Matrix(np.square(predicted - y))
What do you want to differentiate with respect to? You can use loss2.diff(w1[0, 0]) to differentiate with respect to the upper left entry of w1. -- Oscar -- You received this message because you are subscribed to the Google Groups "sympy" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/sympy/CAHVvXxQbuzUZ6bna%3DK5y04c8U0GksoOi2y-Fje7BMwEsiJYkxg%40mail.gmail.com.
