1 # coding:utf8
2 import numpy as np
3 import cPickle
4 import os
5 import tensorflow as tf
6
7 class SoftMax:
8 def __init__(self,MAXT=30,step=0.0025):
9 self.MAXT = MAXT
10 self.step = step
11
12 def load_theta(self,datapath="data/softmax.pkl"):
13 self.theta = cPickle.load(open(datapath,'rb'))
14
15 def process_train(self,data,label,typenum=10,batch_size=500):
16 batches = data.shape[0] / batch_size
17 valuenum=data.shape[1]
18 if len(label.shape)==1:
19 label=self.reshape_data(label,typenum)
20 x = tf.placeholder("float", [None,valuenum])
21 theta = tf.Variable(tf.zeros([valuenum,typenum]))
22 y = tf.nn.softmax(tf.matmul(x,theta))
23 y_ = tf.placeholder("float", [None, typenum])
24 cross_entropy = -tf.reduce_sum(y_*tf.log(y)) #交叉熵
25 train_step = tf.train.GradientDescentOptimizer(self.step).minimize(cross_entropy)
26 init = tf.initialize_all_variables()
27 sess = tf.Session()
28 sess.run(init)
29 for epoch in range(self.MAXT):
30 cost_=[]
31 for index in xrange(batches):
32 c_,_=sess.run([cross_entropy,train_step], feed_dict={ x: data[index * batch_size: (index + 1) * batch_size],
33 y_: label[index * batch_size: (index + 1) * batch_size]})
34 cost_.append(c_)
35 if epoch % 5 == 0:
36 print(( 'epoch %i, minibatch %i/%i,averange cost is %f') %
37 (epoch,index + 1,batches,np.mean(cost_)))
38 self.theta=sess.run(theta)
39 if not os.path.exists('data/softmax.pkl'):
40 f= open("data/softmax.pkl",'wb')
41 cPickle.dump(self.theta,f)
42 f.close()
43 return self.theta
44
45
46 def process_test(self,data,label,typenum=10):
47 valuenum=data.shape[1]
48 if len(label.shape)==1:
49 label=self.reshape_data(label,typenum)
50 x = tf.placeholder("float", [None,valuenum])
51 theta = self.theta
52 y = tf.nn.softmax(tf.matmul(x,theta))
53 y_ = tf.placeholder("float", [None, typenum])
54 init = tf.initialize_all_variables()
55 sess = tf.Session()
56 sess.run(init)
57 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
58 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
59 print "Accuracy: ",sess.run(accuracy, feed_dict={x: data,y_: label})
60
61 def h(self,x):
62 m = np.exp(np.dot(x,self.theta))
63 sump = np.sum(m,axis=1)
64 return m/sump
65
66 def predict(self,x):
67 return np.argmax(self.h(x),axis=1)
68
69 def reshape_data(self,label,typenum):
70 label_=[]
71 for yl_ in label:
72 tl_=np.zeros(typenum)
73 tl_[yl_]=1.0
74 label_.append(tl_)
75 return np.mat(label_)
76
77 if __name__ == '__main__':
78 f = open('mnist.pkl', 'rb')
79 training_data, validation_data, test_data = cPickle.load(f)
80 training_inputs = [np.reshape(x, 784) for x in training_data[0]]
81 data = np.array(training_inputs)
82 training_inputs = [np.reshape(x, 784) for x in validation_data[0]]
83 vdata = np.array(training_inputs)
84 f.close()
85
86 softmax = SoftMax()
87 softmax.process_train(data,training_data[1])
88 softmax.process_test(vdata,validation_data[1]) #Accuracy: 0.9269
89 softmax.process_test(data,training_data[1]) #Accuracy: 0.92718
