CycleGAN-Music-Style-Transfer-Refactorization | Symbolic Music Genre Transfer with CycleGAN | Machine Learning library

by sumuzhao Python Version: Current License: MIT

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

kandi X-RAY | CycleGAN-Music-Style-Transfer-Refactorization Summary

CycleGAN-Music-Style-Transfer-Refactorization is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Qt5, Generative adversarial networks applications. CycleGAN-Music-Style-Transfer-Refactorization has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However CycleGAN-Music-Style-Transfer-Refactorization build file is not available. You can download it from GitHub.

Symbolic Music Genre Transfer with CycleGAN - Refactorization

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CycleGAN-Music-Style-Transfer-Refactorization has a low active ecosystem.

It has 20 star(s) with 8 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 1 have been closed. On average issues are closed in 7 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CycleGAN-Music-Style-Transfer-Refactorization is current.

Quality

CycleGAN-Music-Style-Transfer-Refactorization has no bugs reported.

Security

CycleGAN-Music-Style-Transfer-Refactorization has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

CycleGAN-Music-Style-Transfer-Refactorization 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

CycleGAN-Music-Style-Transfer-Refactorization releases are not available. You will need to build from source code and install.

CycleGAN-Music-Style-Transfer-Refactorization has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Train model
Sets a piano roll to an instrument
Save bars to MIDI file
Writes the piano rolls to a MIDI file
Generate samples for learning
Load numpy array
Convert bars to binary
Compute the absolute difference between pred and target
Create training list
Calculate softmax criterion
Load the origin samples
Build the generator
Run test
This test is used for testing
Writes the given piano roll to a MIDI file

Get all kandi verified functions for this library.

CycleGAN-Music-Style-Transfer-Refactorization Key Features

No Key Features are available at this moment for CycleGAN-Music-Style-Transfer-Refactorization.

CycleGAN-Music-Style-Transfer-Refactorization Examples and Code Snippets

No Code Snippets are available at this moment for CycleGAN-Music-Style-Transfer-Refactorization.

Community Discussions

Trending Discussions on CycleGAN-Music-Style-Transfer-Refactorization

How to use tf.Lambda and tf.Variable at TensorFlow 2.0

QUESTION

How to use tf.Lambda and tf.Variable at TensorFlow 2.0

Asked 2020-Jan-13 at 15:22

I'm very new to TensorFlow 2.0.

I wrote a code for Cyclic GAN as follows (I extract code only for building generator neural network):

...

ANSWER

Answered 2020-Jan-13 at 15:22

Lambda layers are stateless, that is, you cannot define variables within them. Instead, you could rather write a custom layer. Something along the lines of:

Source https://stackoverflow.com/questions/59718635

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

Vulnerabilities

No vulnerabilities reported

Install CycleGAN-Music-Style-Transfer-Refactorization

You can download it from GitHub.
You can use CycleGAN-Music-Style-Transfer-Refactorization 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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