Kryuski
commented
2 years ago I have found out that the same "bad" NN breaks Lazarus compiled program as well. Only need to disable validation during training for this to work with Lazarus:
NFit.Fit(NN, TrainingPairs, nil{ValidationPairs}, TestPairs, {batchsize=}32, {epochs=}50);
I tested this NN on two AMD Ryzen 5 3600 computers with the same result:
Inputs:19.00, 71.00 - Output: 0.00 Desired Output:73.50
Inputs:28.00, 6.00 - Output: 0.00 Desired Output:28.64
Inputs:34.00, 53.00 - Output: 0.00 Desired Output:62.97
Inputs:20.00, 35.00 - Output: 0.00 Desired Output:40.31
Inputs:53.00, 28.00 - Output: 0.00 Desired Output:59.94
Inputs:56.00, 12.00 - Output: 0.00 Desired Output:57.27
Inputs:77.00, 29.00 - Output: 0.00 Desired Output:82.28
Inputs:69.00, 31.00 - Output: 0.00 Desired Output:75.64
Inputs:24.00, 78.00 - Output: 0.00 Desired Output:81.61
Inputs:15.00, 22.00 - Output: 0.00 Desired Output:26.63Now I think this is not related to Delphi, but to the processor. Because sometimes after processing the "bad" NN, subsequent tests of random NN also result in the same error. It would be nice to check the "bad" NN on Intel processors.
joaopauloschuler
While studying the Hypotenuse example program in Delphi, I found out that it cannot train the neural network at all. After 50 epochs, NN returned 0 for any input parameters:
I tried to debug but without result. Finally I tried to add Randomize before creating the network and voila! Just after 10 epochs, a network with a good degree of approximation was built:
I don't know if this library is not fully compatible with Delphi, or the initial weights of the network did not allow it to be trained by this method. If you are interested, here are the initial parameters of the network, which cannot be trained:
And this is the log: autosave.csv https://pastebin.com/kYeUWHRH