我正在学习 FNN 教程，现在它输出 0-1 的 sigmoid 概率（例如 0.8956）。我自己的数据有 3+ 个可能的目标，所以我需要输出是目标列表以及用于测试新样本的相关概率。（在这种情况下，它将是：0（概率），1（概率））。我该怎么办？这是代码。谢谢。

import numpy as np

# sigmoid function
def nonlin(x,deriv=False):
    if(deriv==True):
    return x*(1-x)
    return 1/(1+np.exp(-x))

# input dataset
X = np.array([  [0,0,1],
                [0,1,1],
                [1,0,1],
                [1,1,1] ])

# output dataset            
y = np.array([[0,0,1,1]]).T

# seed random numbers to make calculation
# deterministic (just a good practice)
np.random.seed(1)

# initialize weights randomly with mean 0
syn0 = 2*np.random.random((3,1)) - 1

for iter in xrange(10000):

# forward propagation
l0 = X
l1 = nonlin(np.dot(l0,syn0))

# how much did we miss?
l1_error = y - l1

# multiply how much we missed by the 
# slope of the sigmoid at the values in l1
l1_delta = l1_error * nonlin(l1,True)

# update weights
syn0 += np.dot(l0.T,l1_delta)

print "Output After Training:"
print l1