Thank you for your reply！
um…
I don’t know how to use clone properly (this time I’m not going to delve into this one), so my choice is: clear all variables, restart the program, and restart training. This time I set 【epoch = 1】, manual training. I didn’t change the original code, I just ran it again. I get the change of the weight parameter value in each epoch.
Note: for each epoch, the parameter is updated 1180 times.
I only select a certain weight parameter(I call it weight B) in the model and observe the change of its value in the process of updating.
After the end of each time model training, I will draw the change of weight into a graph. Then, without any changes, retrain.






The model was trained 12 times (manual training), and the above 6 images were obtained. Each graph shows the update of weight B.
It can be seen that in the first five training, the value of weight B has been changing. But in the sixth training, the weight B did not change. From the 6th to the 12th training, the weight B still did not change and remained at -0.5233551.
The following is the 【loss curve】 from the 7th to the 12th. (in order to reduce the space, I merged these 6 images)

Does this mean that since the sixth training, the whole model has not learned anything new from the data, and the model has reached its best state? If so, why are the losses still changing?