Dense tensors store values in a contiguous sequential block of memory where all values are represented.
Tensors or multi-dimensional arrays are used in a diverse set of multi-dimensional data analysis applications.
There are a number of software products that can perform tensor computations, such as the
MATLAB suite that has even been enhanced by various open source third party toolboxes.
MATLAB alone is capable of supporting a variety of element-wise and binary dense tensor operations
A dense layer is a fully connected layer, as each and every neuron gets an input from all the neurons in the previous layer, thus being densely connected. This means that every Neuron in a Dense layer will be fully connected to every Neuron in the prior layer.
Dense is usually used towards the end of a network, and sometimes multiple times.
Trying to build a layered infrastructure for high-performance dense tensor applications, one of the most used libraries is dten, which is known for storing and manipulating dense tensors. The
library focuses on storing dense tensors in canonical storage formats and converting between storage formats in parallel. In addition, it supports tensor matricization in different
ways. The library is general-purpose and provides a high degree of flexibility.
We may regard a tensor as the multidimensional generalization of a matrix. Mathematically, matricization is merely a conceptual (or logical) restructuring of the tensor.