VAE-Tensorflow | Variational Auto-Encoder implementation in Tensorflow | Machine Learning library

by tegg89 Python Version: Current License: MIT

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

kandi X-RAY | VAE-Tensorflow Summary

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

Variational Auto-Encoder implementation in Tensorflow

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VAE-Tensorflow has a low active ecosystem.

It has 10 star(s) with 2 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 996 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of VAE-Tensorflow is current.

Quality

VAE-Tensorflow has 0 bugs and 0 code smells.

Security

VAE-Tensorflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

VAE-Tensorflow code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

VAE-Tensorflow 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

VAE-Tensorflow releases are not available. You will need to build from source code and install.

VAE-Tensorflow has no build file. You will be need to create the build yourself to build the component from source.

It has 140 lines of code, 7 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Loads from checkpoint_dir
Saves model to checkpoint_dir
Build the model

Get all kandi verified functions for this library.

VAE-Tensorflow Key Features

No Key Features are available at this moment for VAE-Tensorflow.

VAE-Tensorflow Examples and Code Snippets

No Code Snippets are available at this moment for VAE-Tensorflow.

Community Discussions

Trending Discussions on VAE-Tensorflow

Why the loss of Variational Autoencoder in many implementations have opposite sign from paper?

QUESTION

Why the loss of Variational Autoencoder in many implementations have opposite sign from paper?

Asked 2017-Aug-08 at 12:54

I think I understand the paper of Auto-Encoding Variational Bayes. And I am reading some tensorflow codes implementing this paper. But I don't understand their loss function in those codes. Since lots of codes are written in same way, probably I am wrong.

The problem is like this. The following equation is from AEVB paper. The loss function is like this equation. This equation can be divided into two: Regularization term and Reconstruction term. Therefore, it becomes

...

ANSWER

Answered 2017-Aug-08 at 12:54

Equation (10) is the log-likelihood loss we want to maximize. It is equivalent to minimizing the negative log-likelihood (NLL). This is what optimization functions do in practice. Note that the Reconstruction_term is already negated in tf.nn.sigmoid_cross_entropy_with_logits (see https://github.com/tegg89/VAE-Tensorflow/blob/master/model.py#L96). We need to negate the Regularization_term as well.

So the code implements Loss_function = -Regularization_term + -Reconstruction_term.

Source https://stackoverflow.com/questions/45564706

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

Vulnerabilities

No vulnerabilities reported

Install VAE-Tensorflow

You can download it from GitHub.
You can use VAE-Tensorflow 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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