DataBloom

Hi all,

I’ve got a Lenovo Legion laptop with an onboard GeForce GTX 1660 GPU. Here’s some setup details:

– Ubuntu 21.10

– Python 3.9.7

– using pip (not Conda)

– Tensorflow 2.7.0 (from Python: “tf.__version__” returns 2.7.0)

– TF doesn’t yet recognize GPU existence: “tf.config.list_physical_devices(‘GPU’) returns []

– I think I have CUDA installed: (cat /proc/driver/nvidia/version):

NVRM version: NVIDIA UNIX x86_64 Kernel Module 495.29.05 Thu Sep 30 16:00:29 UTC 2021

GCC version: gcc version 11.2.0 (Ubuntu 11.2.0-7ubuntu2)

I’m doing a TensorFlow tutorial (with PyTorch to come) & have reached a point where I need the GPU. How can I get TF to recognize it?

Before you ask: yes, I *could* download a Docker container or use Colab. I’m going this route because it seems dumb to have a GPU at my fingertips and not use it.

Thanks all & HNY…

I’ve been trying to install tensorflow on vscode for 2 hours now and no matter what I do It wont do it. If someone knows how to do so can they dm me and help.

hello everyone, I am new to Tensorflow and I try to find a tutorial or something that helps to create new data. My thought was to take the data I got (data of my university from the past 30 years) and look how the AI thinks the university will look like in the next 30 year’s ( so 2052). My main problem is that all the tutorials I did allways say it’s predicting, but actually it’s not exactly what I want. Most tutorial predictions show how accurate an AI can predict data to already existing data. But the thing I want is to create new data, so that I can compare the data my AI created with the data when they get measured. As example the number of students from 1992 till 2021 are already known, but I want the AI to tell me what it calculated fur the years 2022 to 2052. The AI already reads the data and is also pretty accurate, but I’m kinda lost creating the new values. Sorry if I messed up explaining myself but I really hope someone can help me here.

I’ve come across this article and on page 5, it says:

General input/output for SVMs just like for neural nets, but for one important addition… Input: set of (input, output) training pair samples; call the input sample features x1, x2…xn, and the output result y. Typically, there can be lots of input features xi. Output: set of weights w (or wi), one for each feature, whose linear combination predicts the value of y. (So far, just like neural nets…)

It mentions that the y value is calculated through the LC of the weights? Does this mean that x_i • w_i would give me the y value for x_i ∈ X ? If so, how can i calculate the weights w?

If possible, could someone ELI5 this formula on pg 8 for me:

w•xi+b ≥ +1 when yi =+1 w•xi+b ≤ -1 when yi = –1

Thanks