Author: Zen and the Art of Computer Programming
1 Introduction
With the development of deep learning technology and the widespread application in the field of artificial intelligence, more and more people have begun to pay attention to and study how to use neural network training models better, faster and more effectively. The design pattern of neural network is a key part of it. This article will start from researchers' observations and thoughts on neural network design patterns, sort out the latest network design pattern papers, and combine existing toolboxes, frameworks and other methods to sort out design pattern guidelines suitable for practical applications. Based on this, readers can quickly and accurately implement their own neural network models.
2. Background introduction
The early neural network (Neural Network) was composed of multiple neurons, and each neuron communicated through an activation function. However, with the rapid development of deep learning technology, neural network has gradually evolved into a very powerful ability to handle complex high-dimensional data sets, and the improvement of this ability is also accompanied by the emergence of some new design patterns. Since the capabilities of neural networks on different tasks vary greatly, it is difficult to give a general design pattern so that all types of neural networks can achieve better performance. In fact, the design patterns of neural networks have many different factors that affect their performance, such as the number of input features, input data type, output target, loss function, optimization algorithm, number of layers, number of parameters, batch size, regularization, network architecture, network initialization, capturing noise, etc. Therefore, in order to design an excellent neural network model for different tasks, it is necessary to combine relevant knowledge and skills, continuously understand the correlation between these factors, and find the best design scheme that is most suitable for each task.
3. Explanation of basic concepts and terms
(1) Fully Connected
A fully connected neural network means that nodes in each layer are connected to all nodes in the next layer, so the connection between any two layers in the entire network is fully connected. For example, the input layer has only 784 neurons, the hidden layer has 500 neurons, and the output layer has only 10 neurons.