Saving the neural network weights to a csv file is much easier. In my opinion this option is faster and csv tables are easily exported to other databases. Another reason is the great versatility of this approach, since to save to the database, you need to raise the local server, while for the files you just need to allocate a directory.
I’ve tested two options for saving and loading weights. The first option works with a MySQL database, the second saves to a CSV file.
Exist huge number of database management systems (DBMS). Of course, there are graph databases as well. However, for ease of understanding, I settled on MySQL. There are two reasons for this. The first is that I have experience with this DBMS. Secondly, this is a large amount of previously written program code for working with MySQL that I can use in this project.
It is not difficult to maintain the weights. However, there are many factors to consider. For example, maybe need information about which neural network the weights belong to. To do this, necessary to record metadata of the neural network, such as the number of layers, the number of neurons in each layer, what exactly does the neural network do, what is the activation function of the network?
To better understand how the neural network from the book works, reverse engineering is needed. For example, understand how an image library converts an image into a digital set and sends them to the input layer of a neural network?
A neural network is a complex data classifier. Special libraries are used to transfer data, such as sound or image, to the neural network. These libraries provide a digital representation of the object. Weights in the network are represented by numbers from 1 * 10-16 to 0.9.