variational-autoencoder | generate MNIST using a Variational Autoencoder | Machine Learning library

by kvfrans Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | variational-autoencoder Summary

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

generate MNIST using a Variational Autoencoder

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variational-autoencoder has a low active ecosystem.

It has 681 star(s) with 196 fork(s). There are 24 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 1 have been closed. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of variational-autoencoder is current.

Quality

variational-autoencoder has 0 bugs and 0 code smells.

Security

variational-autoencoder has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

variational-autoencoder code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

variational-autoencoder 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.

Reuse

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

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

variational-autoencoder saves you 104 person hours of effort in developing the same functionality from scratch.

It has 264 lines of code, 24 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Return data sets .
Call the model .
Returns the next batch of images .
Initialize the model .
Extract MNIST images from a file .
Extract labels from a MNIST label file .
2D convolutional convolutional layer .
Merge two images .
Download a file .
Create a dense layer .

Get all kandi verified functions for this library.

variational-autoencoder Key Features

No Key Features are available at this moment for variational-autoencoder.

variational-autoencoder Examples and Code Snippets

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Community Discussions

Trending Discussions on variational-autoencoder

Need help in compiling custom loss

Encoder input Different from Decoder Output

how should batch size be customised?

Issue with modifying a Keras class to include call function

Keras AE with split decoder and encoder - But with multiple inputs

QUESTION

Need help in compiling custom loss

Asked 2021-Oct-28 at 17:37

I am adding a custom loss to a VAE, as suggested here: https://www.linkedin.com/pulse/supervised-variational-autoencoder-code-included-ibrahim-sobh-phd/

Instead of defining a loss function, it uses a dense network and takes its output as the loss (if I understand correctly).

...

ANSWER

Answered 2021-Oct-28 at 17:37

There are several ways to implement VAE in Tensorflow. I propose an alternative implementation that can be found in custom_layers_and_models in Tensorflow guide pages :

Let's put all of these things together into an end-to-end example: we're going to implement a Variational AutoEncoder (VAE). We'll train it on MNIST digits.

It uses custom Model classes and the gradient tape. In this way, it is quite easy to add the classifier into the VAE model and add the categorical cross-entropy to the total loss during the optimization.

All you need is to modify:

Source https://stackoverflow.com/questions/69749087

QUESTION

Encoder input Different from Decoder Output

Asked 2021-May-15 at 20:11

Hi Guys I am working with this code from machinecurve

The endecode part has this architecture the input are images with 28x28 size:

...

ANSWER

Answered 2021-May-15 at 13:55

This a problem due to the output shape of your decoder... you can simply solve it by changing the final layer of your decoder with:

Source https://stackoverflow.com/questions/67542051

QUESTION

how should batch size be customised?

Asked 2020-Oct-21 at 22:33

I am running a VAE in Keras. the model compiles, and its summary is :

however, when I try to train the model I get the following error:

...

ANSWER

Answered 2020-Oct-21 at 22:33

There are two things that required to solve the issue:
First, the way to attach the loss function to the model should be by:

Source https://stackoverflow.com/questions/64440273

QUESTION

Issue with modifying a Keras class to include call function

Asked 2020-Sep-17 at 10:53

I want to train a VAE that had a huge dataset and decided to use a VAE code made for fashion MNIST and popular modifications for batch-loading using filenames that I found on github. My research collab notebook is here and a sample section of dataset.

But the way the VAE class is written it does not have a call function which should be there according to keras documentation. I am getting the error NotImplementedError: When subclassing the Model class, you should implement a call method.

...

ANSWER

Answered 2020-Sep-14 at 08:01

APaul31,

Specifically in your code I suggest adding call() function to the VAE class:

Source https://stackoverflow.com/questions/63822281

QUESTION

Keras AE with split decoder and encoder - But with multiple inputs

Asked 2020-Jan-30 at 17:07

I'm trying to train an auto-encoder in keras. In the end I would like to have a separate encoder and decoder models. I can do this for an ordinary AE like here:https://blog.keras.io/building-autoencoders-in-keras.html

However, I would like to train a conditional variant of the model where I pass conditional information to the encoder and the decoder. (https://www.vadimborisov.com/conditional-variational-autoencoder-cvae.html)

I can create the encoder and decoder fine:

...

ANSWER

Answered 2020-Jan-30 at 17:07

Considering that the conditionals are the same for both models

Do this:

Source https://stackoverflow.com/questions/59990142

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

Vulnerabilities

No vulnerabilities reported

Install variational-autoencoder

You can download it from GitHub.
You can use variational-autoencoder 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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