VariationalAutoEncoder | Deep Convolutional Variational Auto Encoder | Machine Learning library

by despoisj Python Version: Current License: No License

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kandi X-RAY | VariationalAutoEncoder Summary

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

Deep Convolutional Variational Auto Encoder implemented in Keras

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VariationalAutoEncoder has a low active ecosystem.

It has 6 star(s) with 2 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

VariationalAutoEncoder has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of VariationalAutoEncoder is current.

Quality

VariationalAutoEncoder has no bugs reported.

Security

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

License

VariationalAutoEncoder 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.

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

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

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VariationalAutoEncoder Key Features

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VariationalAutoEncoder Examples and Code Snippets

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Community Discussions

Trending Discussions on VariationalAutoEncoder

How do I run the tensorflow sample code? (throws an error)

Reading recustructed vector from autoencoder in DL4J

QUESTION

How do I run the tensorflow sample code? (throws an error)

Asked 2020-Jan-09 at 16:24

I am very new to TF2 and tried to customize the example code on the tensorflow guide documentation:

https://www.tensorflow.org/guide/keras/custom_layers_and_models#putting_it_all_together_an_end-to-end_example

The code given in the guide does not run if the latent dimension is set 1, it runs fine for every latent dimension >1!

For training I tried to use the code given in the example but set the latent dim to 1:

...

ANSWER

Answered 2020-Jan-09 at 16:24

OK. The answer came from MarkDoust in Github here

The problem here is the interaction of broadcasting with the z_mean + tf.exp(0.5 * z_log_var) * epsilon line.

Normally the last dimension of z_mean, and z_log_var are known but the last dimension of epsilon is not.

Since you mul and add epsilon it assumes the two have the same shape.

When they have a last dimension of 1, It thinks you might be boradcasting z_mean and z_log_var with epsilon, it can't tell.

So the fix is to tell it that you know the shape of epsilon, and are not broadcastng. Add the following before the z_mean + tf.exp(0.5 * z_log_var) * epsilon line: epsilon.set_shape(z_mean.shape).

Source https://stackoverflow.com/questions/59436710

QUESTION

Reading recustructed vector from autoencoder in DL4J

Asked 2018-Jul-19 at 02:49

My goal is to have an autoencoding network where I can train the identity function and then do forward passes yielding a reconstruction of the input.

For this, I'm trying to use VariationalAutoencoder, e.g. something like:

...

ANSWER

Answered 2018-Jul-19 at 02:49

You can see how to use the VAE layer to extract averaged reconstructions from the variational example.

There's two methods for getting the reconstruction from a variational layer. The standard is generateAtMeanGivenZ Which will draw samples from the layer and give you the average. If you want raw samples you can use generateRandomGivenZ. See the javadoc page for all the other methods.

Source https://stackoverflow.com/questions/51320032

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

Vulnerabilities

No vulnerabilities reported

Install VariationalAutoEncoder

You can download it from GitHub.
You can use VariationalAutoEncoder 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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