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EditorConfig VS Keras

Compare EditorConfig VS Keras and see what are their differences

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EditorConfig logo EditorConfig

EditorConfig is a file format and collection of text editor plugins for maintaining consistent coding styles between different editors and IDEs.

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.
  • EditorConfig Landing page
    Landing page //
    2021-08-25
  • Keras Landing page
    Landing page //
    2023-10-16

EditorConfig features and specs

  • Consistency Across Editors
    EditorConfig helps maintain consistent coding styles for multiple developers working on the same project across various editors and IDEs. This ensures that all developers adhere to the same coding standards, minimizing discrepancies in code formatting.
  • Ease of Use
    EditorConfig files are simple to set up and use. Once the configuration file is in place, any supported editor with the EditorConfig plugin installed will automatically enforce the styles, requiring minimal ongoing maintenance from developers.
  • Compatibility
    EditorConfig is compatible with a wide range of editors and IDEs through plugins, allowing developers to use their preferred development environment while still adhering to project-wide formatting rules.
  • Source Control Friendliness
    By enforcing consistent styles, EditorConfig reduces the likelihood of unnecessary code diffs caused by differing formatting preferences, making version control diffs cleaner and easier to understand.

Possible disadvantages of EditorConfig

  • Limited Scope
    EditorConfig focuses primarily on basic whitespace and file-ending settings. It does not provide comprehensive style enforcement, such as linting for programming language-specific syntax rules or convention enforcement beyond formatting.
  • Requires Editor Support
    EditorConfig requires either native support or plugins to be installed in the editor or IDE. If a developer is using an unsupported editor or does not have the plugin installed, they may not benefit from the configuration.
  • Potential for Inconsistencies
    Depending on the implementation of the EditorConfig plugin in specific editors, there can be slight differences in how rules are applied. This can potentially lead to inconsistencies if not all team members use the same tools or versions.
  • Basic Feature Set
    EditorConfig’s feature set is relatively basic compared to other tools that offer more robust configurations and checks, such as full-featured code linters and formatters that enforce a wider array of coding conventions and rules.

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.

Analysis of Keras

Overall verdict

  • Keras is a solid choice for deep learning projects, offering simplicity and flexibility without sacrificing performance. It is well-suited for educational purposes, research, and even deploying models in production environments.

Why this product is good

  • Keras is widely regarded as a good deep learning library because it provides a user-friendly API that allows for easy and fast prototyping of neural networks. It is built on top of other libraries like TensorFlow, making it robust and efficient for both beginners and experienced developers. Its modularity, extensibility, and compatibility with other tools and libraries make it a popular choice for developing deep learning models.

Recommended for

  • Beginners who are new to deep learning
  • Researchers looking for an easy-to-use platform for prototyping models
  • Developers working on projects that require quick experimentation and development
  • Individuals and companies deploying models into production environments

EditorConfig videos

+ Add

EditorConfig, A tool I include in all my projects

More videos:

  • Review - Detecting missing ConfigureAwait with FxCop and EditorConfig - Dotnetos 5-minute Code Reviews
  • Review - 15 Visual Studio Editor Tips including Intellicode and EditorConfig

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 EditorConfig and Keras)

EditorConfig

Keras

Code Coverage
100 100%
Code Coverage
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Code Analysis
100 100%
Code Analysis
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 EditorConfig and Keras

EditorConfig Reviews

We have no reviews of EditorConfig 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, EditorConfig should be more popular than Keras. It has been mentiond 84 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.

EditorConfig mentions (84)

  • Converting a Git repo from tabs to spaces (2016)
    FWIW: EditorConfig isn't a ".net ecosystem" thing but works across a ton of languages, editors and IDEs: https://editorconfig.org/ Also, rather than using GitHub Actions to validate if it was followed (after branch was pushed/PR was opened), add it as a Git hook (https://git-scm.com/docs/githooks) to run right before commit, so every commit will be valid and the iteration<>feedback loop gets like 400% faster as... - Source: Hacker News / about 1 month ago
  • Config-file-validator v1.7.0 released!
    Added support for EditorConfig, .env, and HOCON validation. - Source: dev.to / 10 months ago
  • C-style: My favorite C programming practices
    There is always .editorconfig [1] to setup indent if you have a directory of files. In places where it really matters (Python) I'll always comment with what I've used. [1] https://editorconfig.org/. - Source: Hacker News / about 1 year ago
  • How to set up a new project using Yarn
    .editorconfig helps maintain consistent coding styles for multiple developers working on the same project across various editors and IDEs. Find more information on the EditorConfig website if you’re curious. - Source: dev.to / about 1 year ago
  • Most basic code formatting
    These are tools that you need to add. But the most elemental code formatting is not here, it is in the widely supported .editorconfig file. - Source: dev.to / about 1 year ago
View more

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 / about 1 month 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
View more

What are some alternatives?

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

Prettier - An opinionated code formatter

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.

ESLint - The fully pluggable JavaScript code quality tool

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

Standard JS - DevOps, Build, Test, Deploy, and Code Review

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

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