vaegan | An implementation of VAEGAN | Machine Learning library

by anitan0925 Python Version: Current License: MIT

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

kandi X-RAY | vaegan Summary

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

This is a code for generating images with VAEGAN (variational autoencoder + generative adversarial net). Its original code is [1]. Our implementation is done using Theano(>=0.8.0rc1).

Support

Quality

Security

License

Reuse

Support

vaegan has a low active ecosystem.

It has 87 star(s) with 18 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 1 have been closed. On average issues are closed in 1 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of vaegan is current.

Quality

vaegan has 0 bugs and 0 code smells.

Security

vaegan has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

vaegan code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

vaegan 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

vaegan releases are not available. You will need to build from source code and install.

vaegan 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.

vaegan saves you 348 person hours of effort in developing the same functionality from scratch.

It has 832 lines of code, 55 functions and 18 files.

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

Top functions reviewed by kandi - BETA

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

Build the encoding layer
Batch norm
2d convolution function
Generate a random uniform parameter
Evaluate the image using postprocessing
Create an Image from arrs
Calculate the cost function
Generate a sequence of minibatches
Load image data from a directory
Process an array
Build the decoder layer
Deprecated function
Build the GAN layer
Print progress bar
Create an image from an array
End the stream
Load configuration from file

Get all kandi verified functions for this library.

vaegan Key Features

No Key Features are available at this moment for vaegan.

vaegan Examples and Code Snippets

No Code Snippets are available at this moment for vaegan.

Community Discussions

Trending Discussions on vaegan

Tensorflow apply_gradients() with multiple losses

QUESTION

Tensorflow apply_gradients() with multiple losses

Asked 2022-Apr-08 at 17:29

I am training a model(VAEGAN) with intermediate outputs and I have two losses,

KL Divergence loss I compute from output layer
Similarity (rec) loss I compute from an intermediate layer.

Can I simply sum them up and apply gradients like below?

...

ANSWER

Answered 2022-Apr-08 at 17:29

Yes, you can generally sum the losses and compute a single gradient. Since the gradient of a sum is the sum of the respective gradients, so the step taken by the summed loss is the same as taking both steps one after another.

Here's a simple example: Say you have two weights, and you are currently at the point (1, 3) ("starting point"). The gradient for loss 1 is (2, -4) and the gradient for loss 2 is (1, 2).

If you apply the steps one after the other, you will first move to (3, -1) and then to (4, 1).
If you sum the gradients first, the overall step is (3, -2). Following this direction from the starting point gets you to (4, 1) as well.

Source https://stackoverflow.com/questions/71800061

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

Vulnerabilities

No vulnerabilities reported

Install vaegan

You can download it from GitHub.
You can use vaegan 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: