tensorflow-style-transfer | concise tensorflow implementation of style | Machine Learning library

by hwalsuklee Python Version: Current License: No License

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

kandi X-RAY | tensorflow-style-transfer Summary

tensorflow-style-transfer is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tensorflow-style-transfer has no bugs, it has no vulnerabilities and it has low support. However tensorflow-style-transfer build file is not available. You can download it from GitHub.

A simple, concise tensorflow implementation of style transfer (neural style)

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Support

tensorflow-style-transfer has a low active ecosystem.

It has 282 star(s) with 104 fork(s). There are 16 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. On average issues are closed in 686 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-style-transfer is current.

Quality

tensorflow-style-transfer has 0 bugs and 0 code smells.

Security

tensorflow-style-transfer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorflow-style-transfer code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tensorflow-style-transfer does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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

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

Installation instructions are not available. Examples and code snippets are available.

tensorflow-style-transfer saves you 113 person hours of effort in developing the same functionality from scratch.

It has 286 lines of code, 20 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Build the graph
Feed forward forward computation
Calculate the sum - product of a tensor
Convolution layer
Pool layer
Argument parser
Check the arguments
Update loss function
Load an image from a file
Saves an image
Reshape an image

Get all kandi verified functions for this library.

tensorflow-style-transfer Key Features

No Key Features are available at this moment for tensorflow-style-transfer.

tensorflow-style-transfer Examples and Code Snippets

No Code Snippets are available at this moment for tensorflow-style-transfer.

Community Discussions

Trending Discussions on tensorflow-style-transfer

What's the difference between Android TensorFlow support and TensorFlow Lite for Android？

QUESTION

What's the difference between Android TensorFlow support and TensorFlow Lite for Android？

Asked 2018-Nov-20 at 14:44

I see a sample in Google codelabs this

it requirements dependencies Android TensorFlow support

...

ANSWER

Answered 2018-Nov-20 at 14:44

The code snippet which you provided corresponds to TensorFlow Mobile.

TensorFlow Mobile is a program useful for running protocol buffers ( .pb ) files on Android , iOS and other IoT stuff. It can only be used to run inferences on a TensorFlow model which is converted to a .pb file. It can only function over specific platforms.
TensorFlow Lite is a successor of TensorFlow Mobile. Lite can run inferences on models which are converted to a .tflite file. The Lite version also allows the developer to run Graphs, Sessions and Tensors over Java and Android. It also provides the Neural Networks API. It can functions over Android and iOS devices, Firebase MLKit, TensorFlow.js and also TensorFlow C++ APIs.

Even Google recommends to use TensorFlow Lite instead of TensorFlow Mobile.

Source https://stackoverflow.com/questions/53394824

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

Vulnerabilities

No vulnerabilities reported

Install tensorflow-style-transfer

You can download it from GitHub.
You can use tensorflow-style-transfer like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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