Pretty Tensor

Enables readable, sequential model definitions that mirror network architecture, as shown in the quick start example where layers like flatten() and fully_connected() are chained.

Acts as a thin wrapper, preserving access to any TensorFlow operation via apply, ensuring no loss of low-level functionality or library compatibility.

Defaults scoping allows setting reused parameters like activation functions in one place, reducing code duplication and improving maintainability.

Sequential mode and subdivision support clear branching for architectures like inception modules, making code visually match the model design.

Custom operations can be added via decorators like @Register, seamlessly integrating into the chain, as demonstrated with the leaky_relu example.