pytorch-vae | PyTorch implementation of Auto-Encoding Variational | Machine Learning library

by kuc2477 Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | pytorch-vae Summary

pytorch-vae is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. pytorch-vae has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

PyTorch implementation of "Auto-Encoding Variational Bayes", arxiv:1312.6114

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pytorch-vae has a low active ecosystem.

It has 27 star(s) with 3 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

pytorch-vae has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pytorch-vae is current.

Quality

pytorch-vae has 0 bugs and 0 code smells.

Security

pytorch-vae has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pytorch-vae code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pytorch-vae 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

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

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

pytorch-vae saves you 169 person hours of effort in developing the same functionality from scratch.

It has 418 lines of code, 23 functions and 6 files.

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

Top functions reviewed by kandi - BETA

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

Train a trained model
Return data loader
Return the Visdom instance
Check if the parameter is on a CUDA device
Visualize a set of scalars
Calculate the KL divergence loss
Compute the BCELoss loss
Sample from the tensor
Saves the checkpoint
Load a checkpoint
Visualize images
Forward the latent code at x
Generate a tensor with zeros
Compute the mean of a feature
Visualize a kernel
Visualize a tensor image
Visualize a scalar

Get all kandi verified functions for this library.

pytorch-vae Key Features

No Key Features are available at this moment for pytorch-vae.

pytorch-vae Examples and Code Snippets

No Code Snippets are available at this moment for pytorch-vae.

Community Discussions

Trending Discussions on pytorch-vae

Loading model from checkpoint is not working

How is KL-divergence in pytorch code related to the formula?

QUESTION

Loading model from checkpoint is not working

Asked 2020-Dec-03 at 02:22

I trained a vanilla vae which I modified from this repository. When I try and use the trained model I am unable to load the weights using load_from_checkpoint. It seems there is a mismatch between my checkpoint object and my lightningModule object.

I have setup an experiment (VAEXperiment) using pytorch-lightning LightningModule. I try to load the weights into the network with:

...

ANSWER

Answered 2020-Aug-04 at 12:45

Posting the answer from comments:

Source https://stackoverflow.com/questions/63243359

QUESTION

How is KL-divergence in pytorch code related to the formula?

Asked 2020-May-04 at 20:57

In VAE tutorial, kl-divergence of two Normal Distributions is defined by:

And in many code, such as here, hereand here, the code is implemented as:

...

ANSWER

Answered 2020-May-04 at 20:57

The expressions in the code you posted assume X is an uncorrelated multi-variate Gaussian random variable. This is apparent by the lack of cross terms in the determinant of the covariance matrix. Therefore the mean vector and covariance matrix take the forms

Using this we can quickly derive the following equivalent representations for the components of the original expression

Substituting these back into the original expression gives

Source https://stackoverflow.com/questions/61597340

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

Vulnerabilities

No vulnerabilities reported

Install pytorch-vae

You can download it from GitHub.
You can use pytorch-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 .

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