What are the main features of Keras?
Keras is an advanced neural network API written in Python that can run on multiple deep learning frameworks such as TensorFlow, Microsoft Cognitive Toolkit, Theano, and others. Here are the main features of Keras:
- User-friendly: Keras offers a simple and consistent API that makes building neural networks easier to use. Its design goal is to make experiments fast, intuitive, and scalable.
- Modularity and scalability: Keras offers a range of modular building blocks that can easily be combined to create complex neural network models. Users can customize and expand these building blocks according to their needs to meet specific task requirements.
- Multiple backend support: Keras is compatible with various deep learning frameworks such as TensorFlow, Theano, and Microsoft Cognitive Toolkit, allowing users to choose the backend that best suits their needs without having to modify their existing Keras code.
- Powerful computational performance: Keras has been optimized for GPU, allowing it to leverage the parallel computing capabilities of GPUs to accelerate the training and inference process of neural networks.
- Built-in tools and models: Keras includes various built-in tools and models that make common deep learning tasks such as image classification, text generation, and object detection easier and faster.
- Keras has a wide community support, with extensive documentation, tutorials, and sample code available for reference. Users can seek help, share experiences, and accelerate learning and problem-solving through the community.
