Comparison of prediction without validation

Today, I tried to change my ANN model with cancelling validation data. I divided all data just into training and testing data and obtain better results.

Summary:

After running different ANN models with different proportions of training and testing data, I get similar results after comparing them. For every model, I may need to run several times to get a best result because R2 of testing data may be very low sometimes.
But for all best results of different proportions of data division,their performance are all similar and not bad.

The first is the best result of 80% training data and 20% testing data.

The second is the best result of 70% training data and 30% testing data.

Although they perform better than last week's results, it is still not good enough. For the global optimization codes online, I think there are some problems of it. Tomorrow I will discuss it with Gowtham to see if there are any solutions.