CNN-text-classification-keras | Text Classification by Convolutional Neural | Machine Learning library

by bhaveshoswal Python Version: v2.0.0 License: No License

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

kandi X-RAY | CNN-text-classification-keras Summary

CNN-text-classification-keras is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Neural Network applications. CNN-text-classification-keras has no bugs, it has no vulnerabilities and it has low support. However CNN-text-classification-keras build file is not available. You can download it from GitHub.

Text Classification by Convolutional Neural Network in Keras

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CNN-text-classification-keras has a low active ecosystem.

It has 213 star(s) with 95 fork(s). There are 8 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 4 have been closed. On average issues are closed in 41 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CNN-text-classification-keras is v2.0.0

Quality

CNN-text-classification-keras has 0 bugs and 2 code smells.

Security

CNN-text-classification-keras has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CNN-text-classification-keras code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CNN-text-classification-keras 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

CNN-text-classification-keras releases are available to install and integrate.

CNN-text-classification-keras 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.

CNN-text-classification-keras saves you 34 person hours of effort in developing the same functionality from scratch.

It has 92 lines of code, 6 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Load and preprocess data
Load data and labels
Clean a string
Build the vocabulary
Pad a list of sentences
Build input data

Get all kandi verified functions for this library.

CNN-text-classification-keras Key Features

No Key Features are available at this moment for CNN-text-classification-keras.

CNN-text-classification-keras Examples and Code Snippets

No Code Snippets are available at this moment for CNN-text-classification-keras.

Community Discussions

Trending Discussions on CNN-text-classification-keras

Setting up a functional model in Keras

QUESTION

Setting up a functional model in Keras

Asked 2018-May-28 at 17:06

I'm just messing around with Keras for fun (continuing education) and am having some issues with specifying the data structure in a CNN.

...

ANSWER

Answered 2018-May-28 at 17:06

You have sparse_categorical_crossentropy which expects just the integer labels of classes whereas you give encoded versions already (18,). As such, you need to change loss='categorical_crossentropy' to fix the problem.

Source https://stackoverflow.com/questions/50570808

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

Vulnerabilities

No vulnerabilities reported

Install CNN-text-classification-keras

You can download it from GitHub.
You can use CNN-text-classification-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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