Patent ID: 11934488
Assignee: CHINA UNIVERSITY OF PETROLEUM (EAST CHINA)
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 4:
5. The method for constructing a digital rock according to claim 3, wherein the using the random sample noise as input, and training the generator network using the trained discriminator network model to obtain a generator network model comprises:
inputting the random sample noise into the generator network to generate a first fake sample;
inputting the first fake sample into the discriminator network model, and calculating a first loss function according to formula Loss_S1=lg(D(G(z,θ)),α)) wherein Loss_S1 denotes the first loss function, D(⋅) denotes the discriminator network model, G(⋅) denotes the generator network, z denotes the random sample noise, a denotes the discriminator network parameter, and θ denotes a generator network parameter;
calculating a generator gradient of each layer of the generator network using the first loss function, and
optimizing the generator loss function using the generator gradient and the mini-batch gradient descent algorithm; going back to the step of “inputting the random sample noise into the generator network to generate a first fake sample” for iteration until a number of iterations reaches a predetermined value or a real or fake probability value of the discriminator network model is a predetermined real or fake probability value; when the number of iterations reaches the predetermined value or the real or fake probability value is the predetermined real or fake probability value, determining corresponding generator network parameters as optimal generator network parameters; and obtaining the generator network model based on the optimal generator network parameters, wherein the generator network parameters are a weight and bias of each layer of the generator network.