def trainOR():
Network0 = NeuralNetwork(sturcture=[2, 3, 1], learningRate=0.5)
for i in range(1000):
Network0.train([0, 0], [0])
Network0.train([0, 1], [1])
Network0.train([1, 0], [1])
Network0.train([1, 1], [1])
print(i, "", round((1 - Network.costFunction) * 100, 0))
pass
print(round((1 - Network0.costFunction) * 100, 2))
Network0.answer([0, 0])
print(Network0.neurons[2][0].getOutput())
pass
def trainXOR():
print("\ntrain XOR")
sturcture = [2, 3, 1]
Network0 = NeuralNetwork(sturcture=sturcture, learningRate=1)
for i in range(10000):
Network0.train([0, 0], [0])
Network0.train([0, 1], [1])
Network0.train([1, 0], [1])
Network0.train([1, 1], [0])
#print(i,"",round((1-Network0.costFunction)*100, 0))
print(i, "",
round(abs(Network0.neurons[len(sturcture) - 1][0].a - 0), 3))
print(Network0.answer([0, 0]))
print(Network0.answer([1, 0]))
print(Network0.answer([0, 1]))
print(Network0.answer([1, 1]))
pass