tensorflow-mnist-VAE | Tensorflow implementation of variational auto | 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-mnist-VAE Summary

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

Tensorflow implementation of variational auto-encoder for MNIST

Support

Quality

Security

License

Reuse

Support

tensorflow-mnist-VAE has a low active ecosystem.

It has 432 star(s) with 172 fork(s). There are 17 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 0 have been closed. On average issues are closed in 901 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-mnist-VAE is current.

Quality

tensorflow-mnist-VAE has 0 bugs and 0 code smells.

Security

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

tensorflow-mnist-VAE code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

tensorflow-mnist-VAE 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-mnist-VAE saves you 171 person hours of effort in developing the same functionality from scratch.

It has 424 lines of code, 21 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-mnist-VAE and discovered the below as its top functions. This is intended to give you an instant insight into tensorflow-mnist-VAE implemented functionality, and help decide if they suit your requirements.

Parse command line arguments
Check the arguments
Download the MNIST dataset
Expand training data
Extract image data
Extracts the labels from the given file
Download a file
A gaussian autoencoder
Gaussian MLP encoder
Bernoulli decoder
Save a scatter plot
Return a discrete colormap
Save images
Merge multiple images

Get all kandi verified functions for this library.

tensorflow-mnist-VAE Key Features

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

tensorflow-mnist-VAE Examples and Code Snippets

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

Community Discussions

Trending Discussions on tensorflow-mnist-VAE

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 tensorflow-mnist-VAE

You can download it from GitHub.
You can use tensorflow-mnist-VAE 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 .

Find more information at: