What are the advantages and disadvantages of Keras?
Keras is a high-level neural network library built on top of lower-level libraries like TensorFlow or Theano, providing a simple and user-friendly API. Here are the advantages and disadvantages of Keras:
Advantages:
- User-friendly: Keras offers a simple and easy-to-understand API, making it easier and more intuitive to build and train neural networks.
- Highly modular: Keras models can be built by simply stacking layers and easily adding or removing them. This modular structure makes it easier to experiment with and modify neural networks.
- Keras provides support for multiple backend libraries such as TensorFlow, Theano, and CNTK, allowing developers to choose a preferred library based on their preferences or needs without having to rewrite their code.
- Community support: Due to being a very popular library, Keras has a large community support which allows developers to seek guidance, troubleshoot problems, and share code.
drawback:
- Limitations on low-level functionality: Keras, being a high-level library, offers many advanced features and abstractions, but may sometimes restrict the implementation of certain low-level operations. For tasks requiring more customization or fine-tuning, it may be necessary to use a lower-level library for greater flexibility.
- Performance issue: While Keras offers high-level abstractions by building on top of low-level libraries, this could lead to some performance sacrifices. Compared to directly using low-level libraries, Keras may be slower. This could be a concern for tasks with very high performance requirements.
- Not suitable for large-scale distributed training: Due to Keras’ design focus on simplicity and ease of use, it may not be suitable for large-scale distributed training. Tasks that require training on multiple machines or clusters may require the use of lower-level libraries for better performance and scalability.
