def main():
'''Experiment on NN tos simulate y= f(x1, x2) = sin(2*pi*x1)*sin(2*pi*x2)'''
'''Case 11: randomly generated inputs with random weights and biases
Optimized to yield the best network performance
'''
datafile = './data/perf/data.txt'
testfile = './data/perf/test.txt'
trainvcsv = './data/perf/training.csv'
testcsv = './data/perf/testing.csv'
imgfile = './data/perf/loss.png'
wbfile = './data/perf/weights_biases.txt'
dm.makeDir('./data/perf')
dm.genRandData(datafile, 50)
dm.genData(testfile, 10)
perf_number = 0
max_perf = 0
while perf_number < 75:
dataset = []
dm.parseData(datafile, dataset)
NN = NeuralNet(3, True) # create NN with 3 hidden neurons
NN.savewb('Weights and Biases before Training', wbfile)
NN.train(dataset, imgfile, trainvcsv, 500) # train the neural network
NN.savewb('Weights and Biases after Training', wbfile)
testset = []
dm.parseData(testfile, testset)
perf_number = NN.test(testset, testcsv)
if perf_number > max_perf:
max_perf = perf_number
print(f'\n\nMax perf: {max_perf}\n\n')
with open(wbfile, 'a+') as F:
F.write(f'\n\nMax perf: {perf_number}\n\n')
