What are the advantages and disadvantages of Keras?
Keras is a high-level deep learning framework used for building neural network models, which comes with its own advantages and disadvantages.
Advantages:
- Easy to use: Keras offers a simple API that allows users to easily build, train, and test neural network models without needing to delve into the underlying implementation details.
- Flexibility: Keras offers a variety of deep learning models, including sequential models, functional models, and subclassing models, allowing users to choose the most suitable model type according to their needs.
- Support for multiple deep learning frameworks: Keras can run on various deep learning frameworks such as TensorFlow, Theano, and Microsoft Cognitive Toolkit, allowing users to choose the appropriate backend engine according to their needs.
- Community Support: Keras has a large user and developer community, where users can seek help and support by reading documentation, participating in discussions, and viewing example code.
- Scalability: Keras offers a wide range of layers and model interfaces, allowing users to easily customize and expand models to achieve more complex neural network structures.
Drawback:
- Performance is relatively low: Compared to low-level deep learning frameworks like TensorFlow and PyTorch, Keras may have lower performance, especially when dealing with large-scale data and complex models.
- Functionality is relatively limited: Keras, while providing a simple and easy-to-use API, may not be as advanced in certain functions and features compared to other deep learning frameworks like TensorFlow and PyTorch.
- Customizability is limited: Although Keras offers a variety of layers and model interfaces, in some cases, users may need to customize more complex neural network structures, which might have certain limitations.
