nn = NeuralNetwork(1, 1)
nn.add(5, [tanh, Dtanh])
nn.add(5, [tanh, Dtanh])
nn.init([linear, Dlinear])
n = 25000
cost = []
for i in range(n):
index = np.random.randint(0, N)
x = data[index][0]
y = data[index][1]
c = nn.cost(x, y)
cost.append(c)
nn.train(x, y)
if i % 100 == 0:
print("Iter " + str(i) + " Cost " + str(c))
nnG = []
sin = []
for i in range(N):
x = i*np.pi*2/N
nnG.append(nn.predict(x)[0])
sin.append(np.sin(x))
c = 0
for i in range(len(nnG)):
