Adversarial_Autoencoder | A wizard 's guide to Adversarial Autoencoders | Machine Learning library

by Naresh1318 Python Version: Current License: MIT

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

kandi X-RAY | Adversarial_Autoencoder Summary

Adversarial_Autoencoder is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. Adversarial_Autoencoder 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.

A wizard's guide to Adversarial Autoencoders

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Quality

Security

License

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Adversarial_Autoencoder has a low active ecosystem.

It has 395 star(s) with 116 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 4 have been closed. On average issues are closed in 65 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Adversarial_Autoencoder is current.

Quality

Adversarial_Autoencoder has 0 bugs and 3 code smells.

Security

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

Adversarial_Autoencoder code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Adversarial_Autoencoder 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

Adversarial_Autoencoder 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.

Adversarial_Autoencoder saves you 325 person hours of effort in developing the same functionality from scratch.

It has 781 lines of code, 34 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Generate image grid
Transformer encoder
Create the results folder
Compute the dense matrix
Compute discriminator
Discrete discriminator
Computes the discriminator_gauss
Transformer decoder
Generate next batch
Convolution layer

Get all kandi verified functions for this library.

Adversarial_Autoencoder Key Features

No Key Features are available at this moment for Adversarial_Autoencoder.

Adversarial_Autoencoder Examples and Code Snippets

No Code Snippets are available at this moment for Adversarial_Autoencoder.

Community Discussions

Trending Discussions on Adversarial_Autoencoder

How to execute multiple training operation on the same batch using tf.data dataset

QUESTION

How to execute multiple training operation on the same batch using tf.data dataset

Asked 2018-Sep-12 at 17:28

I want to implement the code available here using a tf.data dataset and iterator (adversarial autoencoder).

My question is how to use the same batch for multiple training ops?

I need to perform three different training operations on the same batch, however, if I use the tf.data iterator, it does not work on the same batch but on consecutive ones.

...

ANSWER

Answered 2018-Sep-12 at 17:28

Suppose data = db_iter.get_next(), where db_iter is the iterator you are using from tf.data.

I suppose you are traning the 3 ops in 3 different sess.run statements. In that case they will all use 3 different batches as data will be evaluated 3 times.

The fact is that, if the input to each of the three training ops are provided from data as defined above, and run within each sess.run, they will all use the same batch.

Source https://stackoverflow.com/questions/52300157

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

Vulnerabilities

No vulnerabilities reported

Install Adversarial_Autoencoder

You can download it from GitHub.
You can use Adversarial_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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