nadl | small framework that can perform automatic differentiation | Machine Learning library

by heytanay Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | nadl Summary

nadl is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Numpy, Neural Network applications. nadl has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

A little Automatic Differentiation library that I wrote in Python using only numpy that can calculate first order gradients using AD. This framework is very simple implementation of how other big framworks do gradient calculation using Numerical Automatic Differentiation.

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nadl has a low active ecosystem.

It has 11 star(s) with 3 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of nadl is current.

Quality

nadl has no bugs reported.

Security

nadl has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

nadl is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

nadl releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed nadl and discovered the below as its top functions. This is intended to give you an instant insight into nadl implemented functionality, and help decide if they suit your requirements.

Power of a tensor
Return a numpy array
Backward algorithm
Adds two tensors together
Return the sum op
Sum the tensor
Matrix multiplication

Get all kandi verified functions for this library.

nadl Key Features

No Key Features are available at this moment for nadl.

nadl Examples and Code Snippets

No Code Snippets are available at this moment for nadl.

Community Discussions

Trending Discussions on nadl

White/ Blue balance error in high resolution with opencv and picamera (V2)

QUESTION

White/ Blue balance error in high resolution with opencv and picamera (V2)

Asked 2020-May-20 at 05:15

my problem is when i set the resolution higher than 640x480, the output colors are only in the bottom part right. The rest of the output has a blueish color.

I have a RaspyberryPi4 with 4GB ram and a PiCamera V2. The CPU usage is not more than ~65% with the highest resolution. The same error appears also on another rapberrypi and its picamera (V2 NOIR).

Here are the Images (dont care about the white bars in the corner: they came from bad screenshooting)

640x480 - normal

1920x1080 - with error

3280x2464 - with error

Here Is my python script:

...

ANSWER

Answered 2020-May-20 at 05:15

I will answer the question myself: The main problem is the picamera hardware and how the Raspberry is reading it through the Gpu.

The quick solution was to change the resolution to multiples of 32. For the FullHd case it need to be 1920*1088 instead of 1920*1080. Then the colors are normal again.

I also find out the highest resolution before the fps drop drown:
horizontal 1280*704
vertical 640*672
Every higher resolultion will drop the fps from 30+ to ~6-8.

Which part of the camera sensor is detecting/used also depends on the resolution. For more detail read the documentation carefully ;-)

Picamera official Documentation

Source https://stackoverflow.com/questions/60989671

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install nadl

I am currently adding more functionality to the core functions of this framework and this is why the installation for the time being is a little complex but I'll figure it out when I have cleared the back-log of existing tasks. To use this framework, you currently only need to import tensor.py file and it will all work just fine. All other modules are imported into tensor.py so you are covered.
The quickest way to start using nadl:.
Download this repository (or use Github codespaces, your wish) and open it in a terminal/file manager.
Make a python file and import the tensor.py file.
Write your code!

Support

I am currently contemplating the idea of using custom hardware like GPU to fasten the calculations and processing using pyopencl but so far it's just on the white-board.

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