1 # coding:utf8  2 import numpy as np  3 import cPickle  4 import theano  5 import os  6 import theano.tensor as T  7   8 class SoftMax:  9     def __init__(self,MAXT=50,step=0.15,landa=0): 10         self.MAXT = MAXT 11         self.step = step 12         self.landa = landa  #在此权重衰减项未能提升正确率 13          14     def load_theta(self,datapath): 15         self.theta = cPickle.load(open(datapath,‘rb‘)) 16  17     def process_train(self,data,label,typenum,batch_size=500): 18         valuenum=data.shape[1] 19         batches =  data.shape[0] / batch_size 20         data = http://www.mamicode.com/theano.shared(np.asarray(data,dtype=theano.config.floatX)) 21         label = T.cast(theano.shared(np.asarray(label,dtype=theano.config.floatX)), ‘int32‘) 22         x = T.matrix(‘x‘) 23         y = T.ivector(‘y‘) 24         index = T.lscalar() 25         theta =  theano.shared(value=http://www.mamicode.com/0.001*np.zeros((valuenum,typenum), 26             dtype=theano.config.floatX), 27             name=‘theta‘,borrow=True) 28         hx=T.nnet.softmax(T.dot(x,theta)) 29         cost =  -T.mean(T.log(hx)[T.arange(y.shape[0]), y]) +0.5*self.landa*T.sum(theta ** 2)  #权重衰减项 30         g_theta = T.grad(cost, theta) 31         updates = [(theta, theta - self.step * g_theta)] 32         train_model = theano.function( 33         inputs=[index],outputs=cost,updates=updates,givens={ 34             x: data[index * batch_size: (index + 1) * batch_size], 35             y: label[index * batch_size: (index + 1) * batch_size] 36         },allow_input_downcast=True 37         ) 38         lastcostJ = np.inf 39         stop = False 40         epoch = 0 41         costj=[] 42         while (epoch < self.MAXT) and (not stop): 43             epoch = epoch + 1 44             for minibatch_index in xrange(batches): 45                 costj.append(train_model(minibatch_index)) 46             if np.mean(costj)>=lastcostJ: 47                 print "costJ is increasing !!!" 48                 stop=True 49             else: 50                 lastcostJ=np.mean(costj) 51                 print(( ‘epoch %i, minibatch %i/%i,averange cost is %f‘) % 52                         (epoch,minibatch_index + 1,batches,lastcostJ)) 53         self.theta=theta 54         if not os.path.exists(‘data/softmax.pkl‘): 55             f= open("data/softmax.pkl",‘wb‘) 56             cPickle.dump(self.theta.get_value(),f) 57             f.close() 58         return self.theta.get_value() 59  60     def process_test(self,data,label,batch_size=500): 61         batches = label.shape[0] / batch_size 62         data = http://www.mamicode.com/theano.shared(np.asarray(data,dtype=theano.config.floatX)) 63         label = T.cast(theano.shared(np.asarray(label,dtype=theano.config.floatX)), ‘int32‘) 64         x = T.matrix(‘x‘) 65         y = T.ivector(‘y‘) 66         index = T.lscalar() 67         hx=T.nnet.softmax(T.dot(x,self.theta)) 68         predict = T.argmax(hx, axis=1) 69         errors=T.mean(T.neq(predict, y)) 70         test_model = theano.function( 71         inputs=[index],outputs=errors,givens={ 72             x: data[index * batch_size: (index + 1) * batch_size], 73             y: label[index * batch_size: (index + 1) * batch_size] 74         },allow_input_downcast=True 75         ) 76         test_losses=[] 77         for minibatch_index in xrange(batches): 78             test_losses.append(test_model(minibatch_index)) 79         test_score = np.mean(test_losses) 80         print(( ‘minibatch %i/%i, test error of model %f %%‘) % 81               (minibatch_index + 1,batches,test_score * 100.)) 82  83     def h(self,x): 84         m = np.exp(np.dot(x,self.theta)) 85         sump = np.sum(m,axis=1) 86         return m/sump 87  88     def predict(self,x): 89         return np.argmax(self.h(x),axis=1) 90  91 if __name__ == ‘__main__‘: 92     f = open(‘mnist.pkl‘, ‘rb‘) 93     training_data, validation_data, test_data =http://www.mamicode.com/ cPickle.load(f) 94     training_inputs = [np.reshape(x, 784) for x in training_data[0]] 95     data =http://www.mamicode.com/ np.array(training_inputs) 96     training_inputs = [np.reshape(x, 784) for x in validation_data[0]] 97     vdata =http://www.mamicode.com/ np.array(training_inputs) 98     f.close() 99     softmax = SoftMax()100     softmax.process_train(data,training_data[1],10)101     softmax.process_test(vdata,validation_data[1])102     #minibatch 20/20, test error of model 7.530000 %