for stoxos in stoxoiInBinary:
for stoixeio in range(3):
if (stoxos[stoixeio] == 0):
stoxos[stoixeio] = -1
nn = NeuralNetwork(NumberOfNeuronsNInEveryLayer,
activationFunctionInEveryLayer)
number = check_correct_value_for_training()
if (number == 1 or number == 2):
nn.BuildNN()
if (number == 1):
size = check_correct_value_for_training_size_of_training()
if (size == 1):
nn.inputsAndTargets(eisodoi, stoxoiInBinary, 1, 1)
nn.Gradient_Descent_Training_No_Validating()
elif (size == 2):
nn.inputsAndTargets(eisodoi, stoxoiInBinary, 0.5, 1)
nn.Gradient_Descent_Training_No_Validating()
print('MSE = ', nn.anaklhsh())
elif (size == 3):
nn.inputsAndTargets(eisodoi, stoxoiInBinary, 0, 1)
nn.Gradient_Descent_Training_Testing_Validating()
print('MSE = ', nn.anaklhsh())
elif (number == 2):
nn.inputsAndTargets(eisodoi, stoxoiInBinary, 1, 1)
nn.Gradient_Descent_With_Momentum()
elif (number == 3):
nn.inputsAndTargets(eisodoi, stoxoiInBinary, 0.5, 1)
nn.Conjugate_Gradient()
else:
