import numpy as np import theano.tensor as T import theano import time class Linear_Reg(object):   def __init__(self,x):     self.a = theano.shared(value = np.zeros((1,), dtype=theano.config.floatX),name = 'a')     self.b = theano.shared(value = np.zeros((1,), dtype=theano.config.floatX),name = 'b')     self.result = self.a * x + self.b     self.params = [self.a,self.b]   def msl(self,y):     return T.mean((y - self.result)**2) def regrun(rate,data,labels):   X = theano.shared(np.asarray(data,                  dtype=theano.config.floatX),borrow = True)   Y = theano.shared(np.asarray(labels,                  dtype=theano.config.floatX),borrow = True)   index = T.lscalar() #定義符號化的公式  x = T.dscalar('x')  #定義符號化的公式  y = T.dscalar('y')  #定義符號化的公式  reg = Linear_Reg(x = x)   cost = reg.msl(y)   a_g = T.grad(cost = cost,wrt = reg.a) #計算梯度   b_g = T.grad(cost = cost, wrt = reg.b) #計算梯度  updates=[(reg.a,reg.a - rate * a_g),(reg.b,reg.b - rate * b_g)] #更新參數  train_model = theano.function(inputs=[index], outputs = reg.msl(y),updates = updates,givens = {x:X[index], y:Y[index]})   done = True   err = 0.0   count = 0   last = 0.0   start_time = time.clock()   while done:     #err_s = [train_model(i) for i in xrange(data.shape[0])]     for i in xxx:      err_s = [train_model(i) ]      err = np.mean(err_s)      #print err     count = count + 1     if count > 10000 or err <0.1:       done = False     last = err   end_time = time.clock()   print 'Total time is ：',end_time -start_time,' s' # 5.12s   print 'last error :',err   print 'a value : ',reg.a.get_value() # [ 2.92394467]    print 'b value : ',reg.b.get_value() # [ 1.81334458] if __name__ == '__main__':    rate = 0.01   data = np.linspace(1,10,10)   labels = data * 3 + np.ones(data.shape[0],dtype=np.float64) +np.random.rand(data.shape[0])  regrun(rate,data,labels)