TensorFlow-VAE-GAN-DRAW | generative methods implemented with TensorFlow ( Deep | Machine Learning library

by ikostrikov Python Version: Current License: Apache-2.0

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kandi X-RAY | TensorFlow-VAE-GAN-DRAW Summary

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

A collection of generative methods implemented with TensorFlow (Deep Convolutional Generative Adversarial Networks (DCGAN), Variational Autoencoder (VAE) and DRAW: A Recurrent Neural Network For Image Generation).

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TensorFlow-VAE-GAN-DRAW has a low active ecosystem.

It has 595 star(s) with 174 fork(s). There are 33 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 7 have been closed. On average issues are closed in 187 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of TensorFlow-VAE-GAN-DRAW is current.

Quality

TensorFlow-VAE-GAN-DRAW has 0 bugs and 0 code smells.

Security

TensorFlow-VAE-GAN-DRAW has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

TensorFlow-VAE-GAN-DRAW code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

TensorFlow-VAE-GAN-DRAW is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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

TensorFlow-VAE-GAN-DRAW 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.

Top functions reviewed by kandi - BETA

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

Apply filter
Apply filterbank transformation
Compute the gaussian parameters
Generate and save images to directory
Calculates the cost of the reconstruction cost of the target tensor
Gets the variance of the mean and variance
Define the discriminator
Transformer layer
Update the parameters of the optimizer

Get all kandi verified functions for this library.

TensorFlow-VAE-GAN-DRAW Key Features

No Key Features are available at this moment for TensorFlow-VAE-GAN-DRAW.

TensorFlow-VAE-GAN-DRAW Examples and Code Snippets

No Code Snippets are available at this moment for TensorFlow-VAE-GAN-DRAW.

Community Discussions

Trending Discussions on TensorFlow-VAE-GAN-DRAW

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-VAE-GAN-DRAW

You can download it from GitHub.
You can use TensorFlow-VAE-GAN-DRAW 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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