EasyNN-plus grows multi-layer neural networks from the data in a Grid. The neural network input and output layers are created to match the grid input and output columns. Hidden layers connecting to the input and output layers can then be grown to hold the optimum number of nodes. Each node contains a neuron and its connection addresses. The whole process is automatic.
The grid is produced by importing data from spreadsheet files, tab separated plain text files, comma separated files, bitmap files or binary files. The grid can also be produced manually using theEasyNN-plus editing facilities. Numeric, text, image or combinations of the data types in the grid can be used to grow the neural networks.
The neural networks learn the training data in the grid and they can use the validating data in the grid to self validate at the same time. When training finishes the neural networks can be tested using the querying data in the grid, using the interactive query facilities or using querying data in separate files.
The steps that are required to produce neural networks are automated in EasyNN-plus.
EasyNN-plus produces the simplest neural network that will learn the training data. The graphical editor can be used to produce complex networks.
The EasyNN-plus facilities are shown in this manual.
The first part of this manual covers getting started, samples and projects. Experienced users can skip those sections.