What are the functions and uses of Keras?

Keras is a high-level neural network API used to build deep learning models, and it is an open-source library based on Python. Keras features and uses are as follows: 1. Easy to use: Keras provides a concise API that makes building, debugging, and deploying deep learning models easier. It has an intuitive interface that allows users to quickly get started. 2. Multiple backend support: Keras supports multiple deep learning backends, including TensorFlow, Theano, and CNTK. This means that users can choose different backends according to their needs while enjoying the unified interface and convenience provided by Keras. 3. Multiple network architectures: Keras supports various common deep learning network architectures, including fully connected networks, convolutional neural networks, recurrent neural networks, and deep reinforcement learning, among others. Users can choose different network architectures according to their tasks. 4. Pretrained models and model transfer: Keras provides many pre-trained deep learning models, such as VGG, ResNet, Inception, etc. These models can be fine-tuned or used for transfer learning directly on the user’s task, speeding up the model training efficiency. 5. Advanced feature support: Keras provides many advanced features, such as saving and loading models, training with multiple GPUs, model visualization, and debugging, among others. These features allow users to better understand and manage their models. In conclusion, Keras is a powerful and easy-to-use deep learning library that can be used to build and train various types of neural network models, supporting multiple advanced features and pretrained models.