GAN-keras | Keras source code of many GAN algorithms | Machine Learning library

by bubbliiiing Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | GAN-keras Summary

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

It contains the Keras source code of many GAN algorithms, which can be used to train your own model.

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GAN-keras has a low active ecosystem.

It has 214 star(s) with 76 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 11 open issues and 2 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of GAN-keras is current.

Quality

GAN-keras has 0 bugs and 0 code smells.

Security

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

GAN-keras code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

GAN-keras 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

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

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

GAN-keras saves you 479 person hours of effort in developing the same functionality from scratch.

It has 1129 lines of code, 58 functions and 12 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Load a batch of data
Sample images
Set lr
Read image file
Get a resnet
Resolve identity block
Train the model
Sample random images
Build generator
Load training images
Load an image
Build a resnet model

Get all kandi verified functions for this library.

GAN-keras Key Features

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

GAN-keras Examples and Code Snippets

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

Community Discussions

Trending Discussions on GAN-keras

Keras Architecture is not the same for the saved and loaded model

Add class information to Generator model in keras

Add class information to keras network

DCGAN - Issue in understanding code

QUESTION

Keras Architecture is not the same for the saved and loaded model

Asked 2019-Oct-22 at 16:27

I am currently looking into CycleGAN and im using simontomaskarlssons github repository as my baseline. My problem arises when the training is done and I want to use the saved model to generate new samples. Here the model architecture for the loaded model are different from the initialized generator. The direct link for the saveModel function is here.

When I initialize the generator that does the translation from domain A to B the summary looks like the following (line in github). This is as expected since my input image is (140,140,1) and I am expecting an output image as (140,140,1):

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ANSWER

Answered 2019-Oct-22 at 16:27

When you persist your architecture using model.to_json, the method get_config is called so that the layer attributes are saved as well. As you are using a custom class without that method, the default value for padding is being used when you call model_from_json.

Using the following code for ReflectionPadding2D should solve your problem, just run the training step again and reload the model.

Source https://stackoverflow.com/questions/58488303

QUESTION

Add class information to Generator model in keras

Asked 2018-Aug-31 at 10:00

I want to use condition GANs with the purpose of generated images for one domain (noted as domain A) and by having input images from a second domain (noted as domain B) and the class information as well. Both domains are linked with the same label information (every image of domain A is linked to an image to domain B and a specific label). My generator so far in Keras is the following:

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ANSWER

Answered 2018-Aug-27 at 09:29

At first, following the suggestion which is given in Conditional Generative Adversarial Nets you have to define a second input. Then, just concatenate the two input vectors and process this concatenated vector.

Source https://stackoverflow.com/questions/51989867

QUESTION

Add class information to keras network

Asked 2018-Aug-27 at 08:49

I am trying to figure out how I will use the label information of my dataset with Generative Adversarial Networks. I am trying to use the following implementation of conditional GANs that can be found here. My dataset contains two different image domains (real objects and sketches) with common class information (chair, tree, orange etc). I opted for this implementation which only considers the two different domains as different "classes" for the correspondence (train samples X correspond to the real images while target samples y correspond to the sketch images).

Is there a way to modify my code and take into account the class information (chair, tree, etc.) in my whole architecture? I want actually my discriminator to predict whether or not my generated images from the generator belong to a specific class and not only whether they are real or not. As it is, with the current architecture, the system learns to create similar sketches in all cases.

Update: The discriminator returns a tensor of size 1x7x7 then both y_true and y_pred are passed through a flatten layer before calculating the loss:

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ANSWER

Answered 2018-Jun-22 at 21:15

You should modify your discriminator model, either to have two outputs, or to have a "n_classes + 1" output.

Warning: I don't see in the definition of your discriminator it outputting 'true/false', I see it outputting an image...

Somewhere it should contain a GlobalMaxPooling2D or an GlobalAveragePooling2D.
At the end and one or more Dense layers for classification.

If telling true/false, the last Dense should have 1 unit.
Otherwise n_classes + 1 units.

So, the ending of your discriminator should be something like

Source https://stackoverflow.com/questions/50909007

QUESTION

DCGAN - Issue in understanding code

Asked 2017-Dec-26 at 22:37

This a part of the code for a Deconvolutional-Convoltional Generative Adversarial Network (DC-GAN)

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ANSWER

Answered 2017-Dec-26 at 22:37

Line ganInput = Input(shape=(100,)) is just defining the shape of your input which is a tensor of shape (100,)

The model will include all layers required in the computation of output given input. In the case of multi-input or multi-output models, you can use lists as well:

Source https://stackoverflow.com/questions/47976993

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

Vulnerabilities

No vulnerabilities reported

Install GAN-keras

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