BP神经网络——交叉熵作代价函数

Sigmoid函数

当神经元的输出接近 1时，曲线变得相当平，即σ′(z)的值会很小，进而也就使∂C/∂w和∂C/∂b会非常小。造成学习缓慢，下面有一个二次代价函数的cost变化图，epoch从15到50变化很小。

引入交叉熵代价函数

针对上述问题，希望对输出层选择一个不包含sigmoid的权值更新，使得

由链式法则，得到

由σ′(z) = σ(z)(1− σ(z))以及σ(z)=a，可以将上式转换成

对方程进行关于a的积分，可得

对样本进行平均之后就是下面的交叉熵代价函数

对比之前的输出层delta，相当于去掉了前面的

相应的代码仅改动了一行(58->59)，新的cost变化图如下。

在训练和测试数据各5000个时，识别正确数从4347稍提高到4476。

 

 1 # coding:utf8
 2 import cPickle
 3 import numpy as np
 4 import matplotlib.pyplot as plt
 5 
 6 
 7 class Network(object):
 8     def __init__(self, sizes):
 9         self.num_layers = len(sizes)
10         self.sizes = sizes
11         self.biases = [np.random.randn(y, 1) for y in sizes[1:]]  # L(n-1)->L(n)
12         self.weights = [np.random.randn(y, x)
13                         for x, y in zip(sizes[:-1], sizes[1:])]
14 
15     def feedforward(self, a):
16         for b_, w_ in zip(self.biases, self.weights):
17             a = self.sigmoid(np.dot(w_, a)+b_)
18         return a
19 
20     def SGD(self, training_data, test_data,epochs, mini_batch_size, eta):
21         n_test = len(test_data)
22         n = len(training_data)
23         plt.xlabel('epoch')
24         plt.title('cost')
25         cy=[]
26         cx=range(epochs)
27         for j in cx:
28             self.cost = 0.0
29             np.random.shuffle(training_data)  # shuffle
30             for k in xrange(0, n, mini_batch_size):
31                 mini_batch = training_data[k:k+mini_batch_size]
32                 self.update_mini_batch(mini_batch, eta)
33             cy.append(self.cost/n)
34             print "Epoch {0}: {1} / {2}".format(
35                     j, self.evaluate(test_data), n_test)
36         plt.plot(cx,cy)
37         plt.scatter(cx,cy)
38         plt.show()
39 
40     def update_mini_batch(self, mini_batch, eta):
41         for x, y in mini_batch:
42             delta_b, delta_w,cost = self.backprop(x, y)
43             self.weights -= eta/len(mini_batch)*delta_w
44             self.biases -= eta/len(mini_batch)*delta_b
45             self.cost += cost
46 
47     def backprop(self, x, y):
48         b=np.zeros_like(self.biases)
49         w=np.zeros_like(self.weights)
50         a_ = x
51         a = [x]
52         for b_, w_ in zip(self.biases, self.weights):
53             a_ = self.sigmoid(np.dot(w_, a_)+b_)
54             a.append(a_)
55         for l in xrange(1, self.num_layers):
56             if l==1:
57                 # delta= self.sigmoid_prime(a[-1])*(a[-1]-y)  # O(k)=a[-1], t(k)=y
58                 delta= a[-1]-y  # cross-entropy
59             else:
60                 sp = self.sigmoid_prime(a[-l])   # O(j)=a[-l]
61                 delta = np.dot(self.weights[-l+1].T, delta) * sp
62             b[-l] = delta
63             w[-l] = np.dot(delta, a[-l-1].T)
64         cost=0.5*np.sum((b[-1])**2)
65         return (b, w,cost)
66 
67     def evaluate(self, test_data):
68         test_results = [(np.argmax(self.feedforward(x)), y)
69                         for (x, y) in test_data]
70         return sum(int(x == y) for (x, y) in test_results)
71 
72     def sigmoid(self,z):
73         return 1.0/(1.0+np.exp(-z))
74 
75     def sigmoid_prime(self,z):
76         return z*(1-z)
77 
78 if __name__ == '__main__':
79 
80         def get_label(i):
81             c=np.zeros((10,1))
82             c[i]=1
83             return c
84 
85         def get_data(data):
86             return [np.reshape(x, (784,1)) for x in data[0]]
87 
88         f = open('mnist.pkl', 'rb')
89         training_data, validation_data, test_data = cPickle.load(f)
90         training_inputs = get_data(training_data)
91         training_label=[get_label(y_) for y_ in training_data[1]]
92         data = zip(training_inputs,training_label)
93         test_inputs = training_inputs = get_data(test_data)
94         test = zip(test_inputs,test_data[1])
95         net = Network([784, 30, 10])
96         net.SGD(data[:5000],test[:5000],50,10, 3.0,)   # 4476/5000 (4347/5000)

 

转载于:https://www.cnblogs.com/qw12/p/6107553.html