I have implemented a PyTorch NN code for classification and regression.
Classification:
a) Use stratifiedKfolds for cross-validation (K=10- means 10 fold-cross validation)
I divided the data: as follows:
Suppose I have 100 data: 10 for testing, 18 for validation, 72 for training.
b) Loss function = CrossEntropy
c) Optimization = SGD
d) Early Stopping where waittime = 100 epochs.

Problem is:
Baseline Accuracy = 51%
Accuracy on Training set = 100%
Accuracy on validation set = 90%
Accuracy on testing set = 72%

I don’t understand what are the reasons behind the huge performance difference in Testing data/ Validation data?
How can I solve this problem?
Regression:
a) use the same network structure
b) loss function = MSELoss
c) Optimization = SGD
d) Early Stopping where waittime = 100 epochs.
e) Use K-fold for cross-validation.
I divided the data: as follows:
Suppose I have 100 data: 10 for testing, 18 for validation, 72 for training.
> Problem is:
Baseline MSE= 14.0
Accuracy on Training set = 0.0012
Accuracy on validation set = 6.45
Accuracy on testing set = 17.12
I don’t understand what are the reasons behind the huge performance difference in Testing data/ Validation data?
How can I solve these problems? or Is this an obvious thing for NN/ depend on particular dataset?