Thanks!
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))
On Saturday, May 30, 2020 at 10:27:14 PM UTC+2, David Bailey wrote:
>
> On 30/05/2020 15:02, Giuseppe G. A. Celano wrote:
>
> Enter code here...
>
> I am trying to perform a dot multiplication between a numpy array
> (64,1000) and a sympy matrix (1000, 100) containing only variables, but the
> computation never ends. How to do that?
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> .
>
> That calculation is going to create matrix with 6,400 elements, each of
> which will be a summation of 1000 terms (at least before any possible
> simplification, and assuming you mean variables without a numeric value).
> Bearing in mind that symbolic expressions take quite a lot of memory, that
> lot is going to take up a fair bit of memory but I'd guess it would be OK
> in 64 bits (you don't say if your Python installation is 64bits as opposed
> to 32 bits).
>
> However, calculating something that size is certainly going to be
> challenging, and may need to run over night. In addition there is the
> problem that the program may be choking trying to print the result.
>
> My advice would be to start with a much scaled down example, and then
> gradually scale it up to see what breaks.
>
> If you want to do something immediately to use the matrix without printing
> the 64 x 100 matrix of algebraic expressions (!!) that might be best.
>
> Good luck!
>
> David
>
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