I am currently migrating our app to Python 3.11.6. We updated to tensorflow==2.15.0 and numpy==1.26.4 . I am getting this error message: “NotImplementedError: numpy() is only available when eager execution is enabled”

import numpy as np X = np.arange(28).reshape(4, 7) print(X) print(X[::2,::3]) can please someone explain the print statement for me? I can not figure out what it realy does and what python do with it. With best regards, Reinhold numpy New contributor bernd-reinhold.kwauka is a new contributor to this site. Take care in asking for clarification, […]

I have an AMD Ryzen 7 2700X and I’m trying to compile Numpy in Anaconda virtual environment using the BLIS/Lapack libraries of AMD AOCL 4.2,that I installed locally.
I tried to compile through pip in three different modes, but always without success because via msvc the settings of .pc are not detected, while by gcc the blis is downloaded remotely instead of using the blis/ AOCL that I installed on the PC.

Hello I’m learning numpy, So there is a equation I did in google collab presented by my lecturer. The answer provided by the google collab is ‘[39 58]’. I didnt understand how it calculated, i asked chatgpt, it insist the answer is ‘[29, 67]’. Care to help me in this.

Suppose that I have n functions f1,...,fn where each function takes two numpy ndarrays X,Y of the same size and returns an ndarray Z of the same size. Is there a way to create the ndarray [f1(X,Y),...,fn(X,Y)] without using for loops or list comprehension?

I have some code for solving a problem for a PDE numerically. It was first written in Spyder where it produced the necessary result, but then other people tried to run a direct copy of it in Spyder on their devices and ‘Singular Matrix’ was raised. When I try to run it in Visual Studio Code I get the expected result and no exception is raised. The only difference i can think of for me is that I don’t use any python specific software like Spyder but it seems irrelevant since the code originated from there.
The only function used that may encounter a singular matrix is linalg.solve

Consider the numpy 2d array shown: