l1 = Layer(1,label='input',num_neurons=3) # input layer
l2 = Layer(3,label='output')# output layer
error_out = []
epoch = 2000
# training
for epoch in range(0,epoch):
for v,d in zip(train_in,train_out) :
o1 = l1.inout(v)
o2 = l2.inout(o1)
#back propagation
# output layer
print('output layer')
print('============')
for n in l2.get_neurons() :
e_out = n.update_weight(desired=d[0])
print('\n')
# output layer
