Keras

Keras is a high-level deep learning interface for developers and researchers, positioned as a “rapid iteration layer for model building and experimentation.” It is not a standalone low-level tensor engine; instead, it runs on top of backends such as TensorFlow, JAX, and PyTorch, offering a consistent, streamlined API. Evidence from shared code snippets and tutorials on Zhihu demonstrates its simplicity through keras.layers and keras.models, while its 64,000+ GitHub stars mainly attest to its popularity.

In practice, Keras abstracts neural network development into patterns like Sequential, Functional API, and subclassing, covering common components (dense, convolutional, recurrent, Transformer) and a rich set of pretrained models (e.g., ResNet, BERT) as well as extensions like KerasCV and KerasNLP. Switching backends requires only an environment variable, with official benchmarks showing 20%–350% speedups depending on the backend. The evidence includes hands-on tutorials for tasks such as cat/dog classification, custom loss functions, and BERT pretraining, confirming its utility beyond mere social media hype.