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TypedFlow is a typed, higher-order frontend to TensorFlow and a high-level library for deep-learning.
The main design principles are:
To make the parameters of layers explicit. This choice makes sharing of parameters explicit and allows to implement "layers" as pure functions.
To provide as precise as possible types. Functions are explicit about the shapes and elements of the tensors that they manipulate (they are often polymorphic in shapes and elements though.)
To let combinators be as transparent as possible. If a NN layers is a simple tensor transformation it will be exposed as such.
