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:

1. 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.
2. 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.
3. 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.
4. 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.
5. 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:

1. 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.
2. 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.
3. 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.