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OpenGrok VS Keras

Compare OpenGrok VS Keras and see what are their differences

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Contents:
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  3. » Reviews
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OpenGrok logo OpenGrok

OpenGrok is a fast and usable source code search and cross reference engine.

Keras logo Keras

Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
  • OpenGrok Landing page
    Landing page //
    2021-10-20
  • Keras Landing page
    Landing page //
    2023-10-16

OpenGrok features and specs

  • Efficient Code Search
    OpenGrok provides powerful full-text code search capabilities, which allow developers to quickly find relevant code fragments, classes, and functions across potentially large codebases.
  • Source Code Navigation
    It facilitates easy navigation through source code, enabling users to explore code structure, variable definitions, and references, enhancing understanding and productivity.
  • Supports Multiple Version Control Systems
    OpenGrok is compatible with various version control systems such as Git, Mercurial, and Subversion, making it versatile and adaptable to different development environments.
  • Web Interface
    The tool provides a user-friendly web interface, allowing remote access to code repositories and making it easier for teams to collaborate and share code insights.
  • Cross-Referencing
    OpenGrok includes cross-referencing capabilities that enable developers to identify and analyze code dependencies and connections, improving code comprehension and maintenance.

Possible disadvantages of OpenGrok

  • Initial Setup Complexity
    Setting up OpenGrok can be challenging, requiring considerable configuration and resources, particularly for large and complex codebases.
  • Resource Intensive
    The tool can be resource-intensive, requiring substantial CPU and memory, especially when indexing large repositories, which may impact performance.
  • Limited Language Support
    OpenGrok may not support all programming languages natively for indexing and searching, potentially limiting its applicability in heterogeneous environments.
  • Maintenance Overhead
    Ensuring that OpenGrok remains efficient and up-to-date can entail ongoing maintenance, including regular updates and re-indexing of repositories.
  • Scalability Challenges
    While OpenGrok is powerful, scaling it for very large enterprise environments or numerous users can present challenges, requiring infrastructure considerations and optimizations.

Keras features and specs

  • User-Friendly
    Keras provides a simple and intuitive interface, making it easy for beginners to start building and training models without needing extensive experience in deep learning.
  • Modularity
    Keras follows a modular design, allowing users to easily plug in different neural network components, such as layers, activation functions, and optimizers, to create complex models.
  • Pre-trained Models
    Keras includes a wide range of pre-trained models and offers easy integration with transfer learning techniques, reducing the time required to achieve good results on new tasks.
  • Integration with TensorFlow
    As part of TensorFlow’s ecosystem, Keras provides deep integration with TensorFlow functionalities, enabling users to leverage TensorFlow's powerful features and performance optimizations.
  • Extensive Documentation
    Keras has comprehensive and well-organized documentation, along with numerous tutorials and code examples, making it easier for developers to learn and use the framework.
  • Community Support
    Keras benefits from a large and active community, which provides support through forums, GitHub, and specialized user groups, facilitating the resolution of issues and sharing of best practices.

Possible disadvantages of Keras

  • Performance Limitations
    Due to its high-level abstraction, Keras may incur performance overheads, making it less suitable for scenarios requiring extremely fast execution and low-level optimizations.
  • Limited Low-Level Control
    The simplicity and abstraction of Keras can be a downside for advanced users who need fine-grained control over model components and custom operations, which may require them to resort to lower-level frameworks.
  • Scalability Issues
    In some complex applications and large-scale deployments, Keras might face scalability challenges, where more specialized or low-level frameworks could handle such tasks more efficiently.
  • Dependency on TensorFlow
    While the integration with TensorFlow is generally an advantage, it also means that the performance and features of Keras are closely tied to the development and updates of TensorFlow.
  • Lagging Behind Latest Research
    Keras, being a user-friendly high-level API, might not always incorporate the latest cutting-edge research advancements in deep learning as quickly as more research-oriented frameworks.

OpenGrok videos

+ Add

How to setup Opengrok on Linux (In less than 2 minutes)

More videos:

  • Review - Writing and Rewriting Web Apps in nginx.conf — URL shortening, OpenGrok05 by Constantine Murenin

Keras videos

+ Add

3. Deep Learning Tutorial (Tensorflow2.0, Keras & Python) - Movie Review Classification

More videos:

  • Review - Movie Review Classifier in Keras | Deep Learning | Binary Classifier
  • Review - EKOR KERAS!! Review and Bike Check DARTMOOR HORNET 2018 // MTB Indonesia

Category Popularity

0-100% (relative to OpenGrok and Keras)

OpenGrok

Keras

Git
100 100%
Git
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Code Collaboration
100 100%
Code Collaboration
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare OpenGrok and Keras

OpenGrok Reviews

We have no reviews of OpenGrok yet.
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Keras Reviews

10 Python Libraries for Computer Vision
TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.
Source: clouddevs.com
25 Python Frameworks to Master
Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by François Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.
Source: kinsta.com
15 data science tools to consider using in 2021
Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It's an open source deep learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends but was tied exclusively to TensorFlow starting with...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

Based on our record, Keras seems to be more popular. It has been mentiond 35 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

OpenGrok mentions (0)

We have not tracked any mentions of OpenGrok yet. Tracking of OpenGrok recommendations started around Mar 2021.

Keras mentions (35)

  • Top Programming Languages for AI Development in 2025
    The unchallenged leader in AI development is still Python. And Keras, and robust community support. - Source: dev.to / 22 days ago
  • Top 8 OpenSource Tools for AI Startups
    If you need simplicity, Keras is a great high-level API built on top of TensorFlow. It lets you quickly prototype neural networks without worrying about low-level implementations. Keras is perfect for getting those first models up and running—an essential part of the startup hustle. - Source: dev.to / 7 months ago
  • Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
    At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck. - Source: dev.to / 8 months ago
  • Using Google Magika to build an AI-powered file type detector
    The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models. - Source: dev.to / 12 months ago
  • My Favorite DevTools to Build AI/ML Applications!
    As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / about 1 year ago
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What are some alternatives?

When comparing OpenGrok and Keras, you can also consider the following products

Sourcegraph - Sourcegraph is a free, self-hosted code search and intelligence server that helps developers find, review, understand, and debug code. Use it with any Git code host for teams from 1 to 10,000+.

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

Atlassian Fisheye - With FishEye you can search code, visualize and report on activity and find for commits, files, revisions, or teammates across SVN, Git, Mercurial, CVS and Perforce.

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Krugle - Krugle is the complete enterprise solution for search targeted to the development organization. 

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

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