# -*- coding: utf-8 -*-'''Created on 1月26日@author: Jason.F@summary: pip install Theano，Theano,对于张量能够高效地实现、编译和评估数学表达式，支持在GPU上运行，利用GPU中巨大内存带宽及浮点数运算能力实现一个基于最小二乘法的线性回归'''import theanofrom theano import tensor as Timport numpy as npimport matplotlib.pyplot as pltprint(theano.config.floatX)#查看浮点变量的设置theano.config.floatX='float32'print(theano.config.device)#查看是在CPU还是GPU上X_train =np.asarray([[0.0],[1.0],[2.0],[3.0],[4.0],[5.0],[6.0],[7.0],[8.0],[9.0]],dtype=theano.config.floatX)y_train =np.asarray([1.0,1.3,3.1,2.0,5.0,6.3,6.6,7.4,8.0,9.0],dtype=theano.config.floatX)def train_linreg(X_train,y_train,eta,epochs):costs=[]#initial arrayseta0=T.fscalar('eta0')y=T.fvector(name='y')X=T.fmatrix(name='X')w=theano.shared(np.zeros(shape=(X_train.shape[1]+1),dtype=theano.config.floatX),name='w')#calculate costnet_input = T.dot(X,w[1:])+w[0]errors=y-net_inputcost = T.sum(T.pow(errors,2))#perform gradient updategradient = T.grad(cost,wrt=w)update =[(w,w-eta0*gradient)]#compile modeltrain = theano.function(inputs=[eta0],outputs=cost,updates=update,givens={X:X_train,y:y_train})for _ in range(epochs):costs.append(train(eta))return costs,wcosts,w = train_linreg(X_train, y_train, eta=0.001, epochs=10)plt.plot(range(1,len(costs)+1),costs)plt.tight_layout()plt.xlabel('Epoch')plt.ylabel('Cost')plt.show() def predict_linreg(X,w):Xt=T.matrix(name='X')net_input = T.dot(Xt,w[1:])+w[0]predict = theano.function(inputs=[Xt],givens={w:w},outputs=net_input)return predict(X)plt.scatter(X_train,y_train,marker='s',s=50)plt.plot(range(X_train.shape[0]),predict_linreg(X_train, w),color='gray',marker='o',markersize=4,linewidth=3)plt.xlabel('x')plt.ylabel('y')plt.show()
结果：
