@SangdonLee Many thanks for your response, just one query, when I try to evaluate NetMeasurements using the following code:

validationData = trainingData2b;
accuracy = NetMeasurements[trainedNet, validationData, "Accuracy"]
precision = NetMeasurements[trainedNet, validationData, "Precision"]

I obtain the following strange results: 
1.
<|1 -> 1., 2 -> 1., 3 -> 1., 4 -> 1., 5 -> 1., 6 -> 1., 7 -> 1., 
 8 -> 1., 9 -> 1., 10 -> 1., 11 -> 1., 12 -> 1., 13 -> 1., 14 -> 1., 
 15 -> 1., 16 -> 1., 17 -> 1., 18 -> 1., 19 -> 1., 20 -> 1., 21 -> 1.,
  22 -> 1., 23 -> 1., 24 -> 1., 25 -> 1., 26 -> 1., 27 -> 1., 
 28 -> 1., 29 -> 1., 30 -> 1., 31 -> 1., 32 -> 1., 33 -> 1., 34 -> 1.,
  35 -> 1., 36 -> 1., 37 -> 1., 38 -> 1., 39 -> 1., 40 -> 1., 
 41 -> 1., 42 -> 1., 43 -> 1., 44 -> 1., 45 -> 1., 46 -> 1., 47 -> 1.,
  48 -> 1., 49 -> 1., 50 -> 1.|>

Firstly, I don't think the accuracy could be 1. Secondly, I would have expected some real number between 0 and 1 for the precision (instead I get this strange output). Do you have any idea what is going on here?

@SangdonLee Thanks for your response. Having implemented your suggestions I do note an improvement. One query, do you maybe know how to incorporate the ValidationSet[] built-in function into the type of neural network to increase the accuracy and prevent overfitting? I left highlighted in purple my attempt at including the ValidationSet[]. It does run but I don't think it is set in an optimal way. I attach the revised Notebook.