neural-style-tf | TensorFlow implementation of Neural Style | Machine Learning library

by cysmith Python Version: Current License: GPL-3.0

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

kandi X-RAY | neural-style-tf Summary

neural-style-tf is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. neural-style-tf has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has medium support. However neural-style-tf build file is not available. You can download it from GitHub.

TensorFlow (Python API) implementation of Neural Style

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Quality

Security

License

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Support

neural-style-tf has a medium active ecosystem.

It has 3074 star(s) with 839 fork(s). There are 130 watchers for this library.

It had no major release in the last 6 months.

There are 64 open issues and 41 have been closed. On average issues are closed in 74 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of neural-style-tf is current.

Quality

neural-style-tf has 0 bugs and 0 code smells.

Security

neural-style-tf has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

neural-style-tf code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

neural-style-tf is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

neural-style-tf releases are not available. You will need to build from source code and install.

neural-style-tf has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

neural-style-tf saves you 286 person hours of effort in developing the same functionality from scratch.

It has 692 lines of code, 47 functions and 1 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Render a video
Convert image to RGB format
Read image
Get the image for a given frame
Render a single image
Get a cv2 image
Parse arguments
Normalize weights

Get all kandi verified functions for this library.

neural-style-tf Key Features

No Key Features are available at this moment for neural-style-tf.

neural-style-tf Examples and Code Snippets

No Code Snippets are available at this moment for neural-style-tf.

Community Discussions

Trending Discussions on neural-style-tf

Python Tensorflow under Windows 10

QUESTION

Python Tensorflow under Windows 10

Asked 2019-Apr-11 at 22:37

I am trying to get Tensorflow GPU support going in Python under Windows 10.

What does work;

Download and install Python v3.7.3

...

ANSWER

Answered 2019-Apr-11 at 22:37

For all those with the „DLL load failed“ problem under Windows 10/Python 3.6.x/RTX20xx.

The combination of CUDA 10.0 (not 10.1!), cuDNN 7.5.0 works fine for me (as of 12 April 2019). I also have Visual Studio 2015 installed (but not sure if needed).

Don‘t forget to add the location of the cuDNN *.dll file (it‘s the /bin/ dir in your CUDA dir) to your PATH.

If you have CUDA 10.1, just uninstall it, install 10.0, add the cuDNN files to the 10.0 dir, and reboot.

Tensorflow can be installed using pip install tensorflow-gpu

Source https://stackoverflow.com/questions/55389064

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

Vulnerabilities

No vulnerabilities reported

Install neural-style-tf

tensorflow
opencv
CUDA 7.5+
cuDNN 5.0+
Download the VGG-19 model weights (see the "VGG-VD models from the Very Deep Convolutional Networks for Large-Scale Visual Recognition project" section). More info about the VGG-19 network can be found here.
After downloading, copy the weights file imagenet-vgg-verydeep-19.mat to the project directory.