RNN-Classification | classify text by rnn/lstm , based on TensorFlow r1.0 | Machine Learning library
kandi X-RAY | RNN-Classification Summary
kandi X-RAY | RNN-Classification Summary
RNN-Classification is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Neural Network applications. RNN-Classification has no bugs, it has no vulnerabilities and it has low support. However RNN-Classification build file is not available. You can download it from GitHub.
classify text by rnn/lstm, based on TensorFlow r1.0
classify text by rnn/lstm, based on TensorFlow r1.0
Support
Quality
Security
License
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Support
RNN-Classification has a low active ecosystem.
It has 84 star(s) with 45 fork(s). There are 11 watchers for this library.
It had no major release in the last 6 months.
There are 2 open issues and 11 have been closed. On average issues are closed in 49 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of RNN-Classification is current.
Quality
RNN-Classification has 0 bugs and 0 code smells.
Security
RNN-Classification has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
RNN-Classification code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
RNN-Classification 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.
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RNN-Classification releases are not available. You will need to build from source code and install.
RNN-Classification has no build file. You will be need to create the build yourself to build the component from source.
RNN-Classification saves you 268 person hours of effort in developing the same functionality from scratch.
It has 649 lines of code, 32 functions and 7 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed RNN-Classification and discovered the below as its top functions. This is intended to give you an instant insight into RNN-Classification implemented functionality, and help decide if they suit your requirements.
- Run training .
- Cross validation .
- Train the model .
- Main function .
- Initialize training .
- Runs the graph .
- Batch normalization .
- Creates initializer for LSTM params .
- Predict label .
- Generate an orthogonal homogeneous matrix .
Get all kandi verified functions for this library.
RNN-Classification Key Features
No Key Features are available at this moment for RNN-Classification.
RNN-Classification Examples and Code Snippets
No Code Snippets are available at this moment for RNN-Classification.
Community Discussions
Trending Discussions on RNN-Classification
QUESTION
Building recurrent neural network with feed forward network in pytorch
Asked 2019-Jan-22 at 03:37
I was going through this tutorial. I have a question about the following class code:
...ANSWER
Answered 2018-Jul-03 at 15:37The network is recurrent, because you evaluate multiple timesteps in the example. The following code is also taken from the pytorch tutorial you linked to.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install RNN-Classification
You can download it from GitHub.
You can use RNN-Classification 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.
You can use RNN-Classification 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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