keras-contrib | Keras community contributions | Machine Learning library

by keras-team Python Version: Current License: MIT

X-Ray Key Features Code Snippets(1)Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | keras-contrib Summary

keras-contrib is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Neural Network applications. keras-contrib has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

Keras community contributions

Support

Quality

Security

License

Reuse

Support

keras-contrib has a medium active ecosystem.

It has 1575 star(s) with 655 fork(s). There are 82 watchers for this library.

It had no major release in the last 6 months.

There are 155 open issues and 87 have been closed. On average issues are closed in 120 days. There are 36 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of keras-contrib is current.

Quality

keras-contrib has 0 bugs and 0 code smells.

Security

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

keras-contrib code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-contrib 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

keras-contrib 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, examples and code snippets are available.

keras-contrib saves you 3589 person hours of effort in developing the same functionality from scratch.

It has 7674 lines of code, 378 functions and 116 files.

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

Top functions reviewed by kandi - BETA

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

A DenseNet
Multi - layer dense block
Create a dense network
Convolution block
Wrap a Residual network
Helper function for convolutional convolution
Create a wide residual network
Convolutional CNN
Transition up block
Compute the jaccard distance between two points
Bottleneck bottleneck function
Extract image patches
Calculate the activation layer
Test for replace_import
Build the chain
Connects the convolutional layer
Build the convolution matrix
Basic block layer
Configure VOC
Extracts a list of pascal segmentation images
Parse image segmentation statistics
A WSNet CIFAR layer
Constructs a NASNet large network
Snet network
Configure the COC library
Extracts a list of pairwise segmentation images

Get all kandi verified functions for this library.

keras-contrib Key Features

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

keras-contrib Examples and Code Snippets

Generative Adversarial Network for 3D Image-to-Image Translation,System Setup

Python

Lines of Code : 3

License : No License

Copy

python3 -m venv env
source env/bin/activate
pip install -r requirements.txt

Community Discussions

Trending Discussions on keras-contrib

ModuleNotFoundError: No module named 'keras_contrib'

How to implement hierarchical model in Keras?

Implementing gradient penalty loss with tensorflow 2

QUESTION

ModuleNotFoundError: No module named 'keras_contrib'

Asked 2021-Aug-02 at 20:25

I installed keras_contrib using:

...

ANSWER

Answered 2021-Aug-02 at 18:16

When running 'pip install ' in the command line, it usually print information while installing the module. On some OSs, you can find the download location there. Otherwise, you can use the following command line code to download towards a precise directory:

Source https://stackoverflow.com/questions/68625914

QUESTION

How to implement hierarchical model in Keras?

Asked 2020-Nov-15 at 16:06

I am trying to rebuild the model featured in https://arxiv.org/abs/1709.04250.

The authors break text up into utterances (think of them like sentences), then use bi-directional LSTMs to combine these utterances, then use bi-directional LSTMs again, this time on the series of utterance representations and finish it off by using a CRF layer to predict the labels associated with every utterance.

This is a visual of the model architecture: enter image description here

Here is my attempt, implemented in Keras and using the CRF layer from https://github.com/keras-team/keras-contrib:

...

ANSWER

Answered 2020-Nov-15 at 16:06

I ended up using https://github.com/xuxingya/tf2crf, which is maintained and works with tf2.

Source https://stackoverflow.com/questions/64758746

QUESTION

Implementing gradient penalty loss with tensorflow 2

Asked 2020-Apr-08 at 17:06

Good morning,

I am trying to implement the improved WGAN for 1D data as described on this paper: https://arxiv.org/pdf/1704.00028.pdf

It has been implemented as an example in the keras-contrib github: https://github.com/keras-team/keras-contrib/blob/master/examples/improved_wgan.py Nevertheless, this implementation of the gradient penalty loss is not working anymore with tf2. K.gradients() returns [None].

...

ANSWER

Answered 2020-Apr-08 at 17:06

If you do what is proposed in the UPDATE, tf will just ignore the loss function

With Tensorflow 2, it seems imposible to to this the old way. I finally change the code to adapt it to this way of creating models. What I suggest?

Create the gen/disc models with keras
Join them extending tf.keras.Model class like the WGAN of : https://github.com/timsainb/tensorflow2-generative-models

Source https://stackoverflow.com/questions/61058119

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