OGIT | arago AG Open Graph of IT '' Ontology Hub | Data Manipulation library

by arago JavaScript Version: 2.19.0 License: MIT

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

kandi X-RAY | OGIT Summary

OGIT is a JavaScript library typically used in Institutions, Learning, Education, Utilities, Data Manipulation applications. OGIT has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Open Graph of IT (OGIT) aims to build a semantic representation of all IT and its interaction with business processes and people. Such an endeavour requires a sound data space as a foundation for computational evaluation of this enterprise space. OGIT aims to become the 'IT version' of Google's Knowledge Graph or Facebook's Social Graph. Hence we need a common language for IT entities and relationships between them. OGIT aims to provide a such common language by defining an ontology for the Graph of IT. The OGIT project is not only a hosting place for a specification. It also provides a platform for domain experts to contribute to the ontology. One of the basic principles is: evolution over standardization.

Support

Quality

Security

License

Reuse

Support

OGIT has a low active ecosystem.

It has 25 star(s) with 40 fork(s). There are 40 watchers for this library.

It had no major release in the last 12 months.

There are 13 open issues and 73 have been closed. On average issues are closed in 47 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of OGIT is 2.19.0

Quality

OGIT has no bugs reported.

Security

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

License

OGIT 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

OGIT releases are available to install and integrate.

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

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OGIT Key Features

No Key Features are available at this moment for OGIT.

OGIT Examples and Code Snippets

No Code Snippets are available at this moment for OGIT.

Community Discussions

Trending Discussions on OGIT

Tensorflow with batch size and wrong dimesion

Git not working in NanoServer (in Docker)

Incompatible shapes for Accuracy when fitting tensorflow2 model

QUESTION

Tensorflow with batch size and wrong dimesion

Asked 2020-Nov-19 at 12:36

Here are my code:

...

ANSWER

Answered 2020-Nov-19 at 12:36

The issue is with the weights, Bias, input_x and input_y placeholder size.

Below is the modified code that should resolve your issue.

Source https://stackoverflow.com/questions/64846657

QUESTION

Git not working in NanoServer (in Docker)

Asked 2020-Apr-03 at 19:41

I need to install Git in Nano Server. During my research I saw that Nano Server can't work with MSI files and that's why that I thought I can easily download it in another container (Windows Server Core in my situation) and move the contents of Git to the other container (the Nano Server container) (Idea is from here: https://stefanscherer.github.io/how-to-build-nodejs-nanoserver-image/). I tried with the following dockerfile:

...

ANSWER

Answered 2020-Apr-03 at 04:42

First, you don't have to run the GIt for Windows setup.

You could also try and uncompress the Portable extractable archive PortableGit-2.26.0-64-bit.7z.exe anywhere you want in the image, and then add to the path:

Source https://stackoverflow.com/questions/61000950

QUESTION

Incompatible shapes for Accuracy when fitting tensorflow2 model

Asked 2019-Apr-28 at 02:09

I am running a text generation model (RNN) on Tensorfow 2.0.0-alpha0 and even though I get the loss metric when fitting the model, I get the following error when inserting accuracy:

InvalidArgumentError: Incompatible shapes: [64] vs. [64,200]
[[{{node metrics_4/accuracy/Equal}}]] [Op:__inference_keras_scratch_graph_6491]

I tried to manually define accuracy on a single batch (pre-training):

...

ANSWER

Answered 2019-Apr-28 at 02:09

accuracy isn't a standard argument of Model.fit - it will be accepted under **kwargs which will then be passed to session.run in graph mode. Try metrics=[accuracy].

Source https://stackoverflow.com/questions/55885331

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