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 = MAXT10         self.step = step11         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_size17         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.theta44 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.theta52         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/sump65 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_]=1.074             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 =http://www.mamicode.com/ cPickle.load(f)80     training_inputs = [np.reshape(x, 784) for x in training_data[0]]81     data =http://www.mamicode.com/ np.array(training_inputs)82     training_inputs = [np.reshape(x, 784) for x in validation_data[0]]83     vdata =http://www.mamicode.com/ 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:  0.926989     softmax.process_test(data,training_data[1])  #Accuracy:  0.92718