DeepSymNet Deep Symbol Network Dataset

This dataset is a symbolic network for symbolic regression.

This is a new symbolic network called DeepSymNet proposed by researchers from the Institute of Semiconductors, Chinese Academy of Sciences, to represent symbolic expressions, and the overall framework of DeepSymNet was demonstrated.The first layer is the data, the middle layer is the hidden layer, and the last layer is the output layer.

The hidden layer nodes are composed of operation symbols, including +, -, ×, ÷, sin, cos, exp, log, id, etc., where the id operator is the same as the id operator in EQL.

The number of id operators in each hidden layer is equal to the number of nodes in the previous layer, while other operators appear only once in each hidden layer. The operator id corresponds one-to-one with the nodes in the previous layer, which enables each layer to utilize all the information of the previous layer. Other operators are ordinary operators and are fully connected to the previous layer.

The connection between the id operator and the previous layer is fixed, and the ordinary operator has no connection to the previous layer, or one or two connections, which means that in this network a subnetwork represents a symbolic expression. The more hidden layers an expression occupies, the higher the complexity of the expression. Therefore, the number of hidden layers can be used to roughly measure the complexity of the expression.

But please note that the input layer has a special node "const" to represent constant coefficients in symbolic expressions. Only the edges connected to the "const" node have weights (constant coefficients) to prevent enough constant coefficients from appearing in the symbolic expression.

all in all,DeepSymNet is a complete network that can represent any expression. Solving SR is the process of searching for subnetworks in DeepSymNet.

AI4S case: https://hyper.ai/news/29243