Title: Understanding the Feedforward Artificial Neural Network Model From the Perspective of Network Flow,
Abstract: In recent years, deep learning based on artificial neural network (ANN) has
achieved great success in pattern recognition. However, there is no clear
understanding of such neural computational models. In this paper, we try to
unravel "black-box" structure of Ann model from network flow. Specifically, we
consider the feed forward Ann as a network flow model, which consists of many
directional class-pathways. Each class-pathway encodes one class. The
class-pathway of a class is obtained by connecting the activated neural nodes
in each layer from input to output, where activation value of neural node
(node-value) is defined by the weights of each layer in a trained
ANN-classifier. From the perspective of the class-pathway, training an
ANN-classifier can be regarded as the formulation process of class-pathways of
different classes. By analyzing the the distances of each two class-pathways in
a trained ANN-classifiers, we try to answer the questions, why the classifier
performs so? At last, from the neural encodes view, we define the importance of
each neural node through the class-pathways, which is helpful to optimize the
structure of a classifier. Experiments for two types of ANN model including
multi-layer MLP and CNN verify that the network flow based on class-pathway is
a reasonable explanation for ANN models.