DCEC | Keras implementation for ICONIP-2017 paper | Machine Learning library

by XifengGuo Python Version: Current License: MIT

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

kandi X-RAY | DCEC Summary

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

Keras implementation for ICONIP-2017 paper:.

Support

Quality

Security

License

Reuse

Support

DCEC has a low active ecosystem.

It has 171 star(s) with 65 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 10 have been closed. On average issues are closed in 68 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of DCEC is current.

Quality

DCEC has 0 bugs and 0 code smells.

Security

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

DCEC code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DCEC 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

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

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

Installation instructions are not available. Examples and code snippets are available.

DCEC saves you 118 person hours of effort in developing the same functionality from scratch.

It has 298 lines of code, 17 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Train the hyperparameters
Calculates the accorrelation coefficient
Train the model
Predict value for the model
R Compute the target distribution
Loads weights from the model
Compile the model
A CAE
Calculate the accorrelation coefficient
Load MNIST dataset
Predict value
Extract a feature

Get all kandi verified functions for this library.

DCEC Key Features

No Key Features are available at this moment for DCEC.

DCEC Examples and Code Snippets

No Code Snippets are available at this moment for DCEC.

Community Discussions

Trending Discussions on DCEC

remove all the EOFs (extra empty lines) at the end of jsonl files

QUESTION

remove all the EOFs (extra empty lines) at the end of jsonl files

Asked 2020-Jun-07 at 14:59

I am working with jsonl files that look something like this in VSCode editor:

first.jsonl

...

ANSWER

Answered 2020-Jun-07 at 14:16

Just to respond to your last code snippet.

You can change the line

Source https://stackoverflow.com/questions/62246370

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

Vulnerabilities

No vulnerabilities reported

Install DCEC

You can download it from GitHub.
You can use DCEC 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 .

Find more information at: