Generating and managing deep tensor neural networks

A computer-implemented method, comprising:

• generating, by a system operatively coupled to a processor, a neural network comprising a graph of nodes connected via weighted edges;
• in response to the neural network receiving input data and parameters, formatting, by the system, the input data and the parameters to generate tensor-formatted input data and tensor-formatted parameters, and wherein at least a portion of the input data is multi-dimensional data;
• extracting, by the system, a first portion of features from the tensor-formatted input data in parallel with extraction of a second portion of the features from the tensor-formatted input data;
• and
• training, by the system, the neural network using a Hamiltonian forward propagation process to evolve the tensor-formatted input data based on a defined tensor-tensor layer evolution rule, wherein the neural network generates output data based on the evolving of the tensor-formatted input data, wherein the Hamiltonian forward propagation process comprises a multiplication of a circulant function of a first antisymmetric and an unfold function of a second matrix.

A computer program product that facilitates creating a deep tensor neural network, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions are executable by a processor to cause the processor to:

• create the deep tensor neural network comprising a graph of nodes connected via weighted edges;
• in response to the neural network receiving input data and parameters, format the input data and the parameters to generate tensor-formatted input data and tensor-formatted parameters, and wherein at least a portion of the input data is multi-dimensional data;
• extract a first portion of features from the tensor-formatted input data in parallel with extraction of a second portion of the features from the tensor-formatted input data;
• and
• training the deep tensor neural network using a Hamiltonian forward propagation process to evolve tensor-structured input data applied to the deep tensor neural network based on a defined tensor-tensor layer evolution rule, wherein the deep tensor neural network generates output data based on the evolving of the tensor-structured input data, wherein the Hamiltonian forward propagation process comprises a multiplication of a circulant function of a first antisymmetric and an unfold function of a second matrix.