Code issues of the Transfer learning about deep-learning-for-indentation HOT 3 OPEN

1. Yes. If you have those data, you can also train on them.
2. You can use this function
   

   deep-learning-for-indentation/src/nn.py

   Line 217 in 42033c3

   def validation_exp_cross(yname):

   
   Basically, the high-fideliyt is 3D FEM + Exp.
3. They are pretty similar. The cross validation data is slightly different.

from deep-learning-for-indentation.

Thanks a lot for your reply.

1. It seems that the function "validation_exp_cross3" is designed for predict the "E, sigmay, sigma033" of "expdata2" by using the network built with the "FEMdata", "BerkovichData" and "dataexp1". While, the "validation_exp_cross" can only perform a 2DFEM+3DFEM training network and predict the expdata.

2. For the function "validation_exp_cross3" in nn.py, I don't understand the purpose of setting the the 10 times of interation. In additon, the result data is presented in 10 columns. I am not sure which column can be the solution with highest reliability.

from deep-learning-for-indentation.

Because neural network algorithm has randomness.

from deep-learning-for-indentation.