Keras

Keras provides layers, model composition and standard fitting and evaluation interfaces. Sequential models suit simple stacks, functional models express connected computation graphs and subclassing allows more custom behavior. Backend selection determines which numerical framework executes compatible operations. Optimizers, losses, metrics and callbacks organize training, while saved artifacts require architecture and state that can be reconstructed. Keras is not identical to TensorFlow, although many existing workflows use them together. Competence includes understanding which operations remain portable and when backend-specific code, custom layers or serialization conventions introduce additional constraints on reuse and deployment.

Choose the simplest model interface that expresses the required computation, verify input and output shapes and distinguish the optimization loss from reporting metrics. Configure callbacks and development evaluation deliberately, checking masks and train/evaluation behavior. For custom code, use compatible operations and test the intended backend. Save and reload the complete model with its preprocessing contract, then evaluate the reloaded artifact. The deliverable should document backend, dependencies and custom components, making clear whether portability was actually tested rather than assumed from the API name.

Backend-specific operations can undermine portability, and custom components may require explicit serialization support. Default metrics can be inappropriate for imbalance or decision costs. Mixing incompatible API and backend versions can cause subtle failures. A model that reloads may still depend on undocumented preprocessing. Keras differs from a complete data or deployment platform and from the neural architecture built with it. Validate the actual backend and saved artifact, including masks, custom losses and input conventions, before treating high-level convenience as correctness.