gru-svm | ICMLC 2018 ] A Neural Network Architecture Combining | Machine Learning library

by AFAgarap Python Version: v0.3.11-alpha License: AGPL-3.0

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

kandi X-RAY | gru-svm Summary

gru-svm is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Neural Network applications. gru-svm has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub, GitLab.

[ICMLC 2018] A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection

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gru-svm has a low active ecosystem.

It has 117 star(s) with 38 fork(s). There are 10 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 6 have been closed. On average issues are closed in 166 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of gru-svm is v0.3.11-alpha

Quality

gru-svm has 0 bugs and 0 code smells.

Security

gru-svm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

gru-svm code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

gru-svm is licensed under the AGPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

gru-svm releases are available to install and integrate.

Build file is available. You can build the component from source.

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

gru-svm saves you 429 person hours of effort in developing the same functionality from scratch.

It has 1017 lines of code, 41 functions and 19 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Save the predicted labels
Normalize the csv files
List all files under path
Runs prediction
Run prediction on the given examples
Plot the confusion matrix
Batch binning data
Parse command line arguments
Converts a txt file to a CSV
Load the data into memory
Save dataframe to a csv file
Export csv to a npy file

Get all kandi verified functions for this library.

gru-svm Key Features

No Key Features are available at this moment for gru-svm.

gru-svm Examples and Code Snippets

No Code Snippets are available at this moment for gru-svm.

Community Discussions

Trending Discussions on gru-svm

Tensor Shape Error: Must be rank 2 but is rank 3

QUESTION

Tensor Shape Error: Must be rank 2 but is rank 3

Asked 2017-Aug-01 at 15:18

I am having difficulty searching for documentation, studies, or blogs that can help me in building text sequence (features) classifier. The text sequence that I have contains logs of network.

I am building a GRU model using TensorFlow, with an SVM as the classification function. I am having trouble with the tensor shapes. It says ValueError: Shape must be rank 2 but is rank 3 for 'MatMul' (op: 'MatMul') with input shapes: [?,23,1], [512,2]. Here is a sample of the data I am using for training my neural network.

The goal of my project is to use this GRU-SVM model for intrusion detection on Kyoto University's honeypot system intrusion detection dataset. The dataset has 23 features, and a label (if there is an intrusion in the network or none).

...

ANSWER

Answered 2017-Aug-01 at 12:25

The problem is in the line:

Source https://stackoverflow.com/questions/45437572

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

Vulnerabilities

No vulnerabilities reported

Install gru-svm

You can download it from GitHub, GitLab.
You can use gru-svm 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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