keras-dcgan | Keras implementation of Deep Convolutional Generative | Machine Learning library

by jacobgil Python Version: Current License: No License

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kandi X-RAY | keras-dcgan Summary

keras-dcgan is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Generative adversarial networks applications. keras-dcgan has no bugs, it has no vulnerabilities and it has medium support. However keras-dcgan build file is not available. You can download it from GitHub.

Keras implementation of Deep Convolutional Generative Adversarial Networks

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keras-dcgan has a medium active ecosystem.

It has 951 star(s) with 412 fork(s). There are 41 watchers for this library.

It had no major release in the last 6 months.

There are 22 open issues and 5 have been closed. On average issues are closed in 51 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of keras-dcgan is current.

Quality

keras-dcgan has 0 bugs and 0 code smells.

Security

keras-dcgan has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-dcgan code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-dcgan 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.

Reuse

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

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

keras-dcgan saves you 56 person hours of effort in developing the same functionality from scratch.

It has 146 lines of code, 7 functions and 1 files.

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

Top functions reviewed by kandi - BETA

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

Train the neural network
Constructs a discriminator model
Creates a generator of model
Combine generated images
Construct a model containing a discriminator
Generate random image
Parse command line arguments

Get all kandi verified functions for this library.

keras-dcgan Key Features

No Key Features are available at this moment for keras-dcgan.

keras-dcgan Examples and Code Snippets

No Code Snippets are available at this moment for keras-dcgan.

Community Discussions

Trending Discussions on keras-dcgan

TensorFlow MNIST DCGAN: how to set up the loss function?

QUESTION

TensorFlow MNIST DCGAN: how to set up the loss function?

Asked 2017-May-12 at 13:08

I would like to build a DCGAN for MNIST by myself in TensorFlow. However, I'm struggling to find out how I should set up the loss function for the generator. In a Keras DCGAN implementation the author used a little "workaround" for this problem: he simply built 3 models. The generator (G), the discriminator (D) and third one, where he just combined G with D, while setting the train-ability of D to false there.

This way, he can feed D with real images + generated images to train D and train the G+D-combined model, because the loss of D is propagated to G, since D is not trainable in the G+D-combined model.

In TensorFlow, I've built G and D already. Training D is relatively simple, since I just need to combine a batch of real MNIST training images with generated ones and call the training op:

...

ANSWER

Answered 2017-May-12 at 13:08

In the generator step training, you can think that the network involves the discriminator too. But to do the backpropagation, you will only consider the generator weights. A good explanation for it is found here.

As mentioned in original paper, the Discriminator cost is:

And the generator cost is:

Of course, you don't need to calculate it by hand. Tensorflow already handles it. To do all the process, you can implement the following:

Source https://stackoverflow.com/questions/43622771

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

Vulnerabilities

No vulnerabilities reported

Install keras-dcgan

You can download it from GitHub.
You can use keras-dcgan 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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