Tenncor
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Tenncor libraries help developers build and evaluate tensor equations and its derivatives. A tensor is an N-dimensional container that organizes its content by some shape. An M by N matrix for instance, is a 2-dimensional tensor with a shape of [N, M] (according to Tenncor's x-y-z-... coordinate notation).
High-level diagram available: https://drive.google.com/file/d/1PrsFa7Duj4Whlu_m0lmFr5JGikGnU3gC/view?usp=sharing
This module supplies syntax tree for equation and generates derivative. Constraints to the equation is limited to each tensor's shape.
This module is contains debug libraries for TEQ Graphs.
This module is implements basic operations for Tenncor's TEQ Tensor objects generated through pybinder. Additionally, ETEQ also defines data format and (de)serialization methods required by PBM.
This module specifies graph optimization through TEQ's visitor pattern.
This module marshals any TEQ graph, but requires data serialization functors when saving and loading.
This module tags TEQ tensors with labels.
This module implements session that updates graph nodes concurrently
This module implements common machine learning models
Tenncor uses bazel 0.28+.
Download bazel: https://docs.bazel.build/versions/master/install.html