PyGOP | python package that implements various algorithms

PyGOP is a Python library. PyGOP has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install PyGOP' or download it from GitHub, PyPI.

[Generalized Operational Perceptron] is an artificial neuron model that was proposed to replace the traditional McCulloch-Pitts neuron model. While standard perceptron model only performs a linear transformation followed by non-linear thresholding, GOP model encapsulates a diversity of both linear and non-linear operations (with traditional perceptron as a special case). Each GOP is characterized by learnable synaptic weights and an operator set comprising of 3 types of operations: nodal operation, pooling operation and activation operation. The 3 types of operations performed by a GOP loosely resemble the neuronal activities in a biological learning system of mammals in which each neuron conducts electrical signals over three distinct operations:. By defining a set of nodal operators, pooling operators and activation operators, each GOP can select the suitable operators based on the problem at hand. Thus learning a GOP-based network involves finding the suitable operators as well as updating the synaptic weights. The author of GOP proposed Progressive Operational Perceptron (POP) algorithm to progressively learn GOP-based networks. Later, [Heterogeneous Multilayer Generalized Operational Perceptron (HeMLGOP)] algorithm and its variants (HoMLGOP, HeMLRN, HoMLRN) were proposed to learn heterogeneous architecture of GOPs with efficient operator set search procedure. In addition, fast version of POP, i.e., [POPfast] was proposed together with memory extensions [POPmemO] [POPmemH] that augment POPfast by incorporating memory path.