# -*- coding: utf-8 -*- import theano import theano.tensor as T import numpy as np from sklearn import datasets import matplotlib.pyplot as plt import time #定义数据类型 np.random.seed(0) train_X, train_y = datasets.make_moons(300, noise=0.20) train_X = train_X.astype(np.float32) train_y = train_y.astype(np.int32) num_example=len(train_X) #设置参数 nn_input_dim=2 #输入神经元个数 nn_output_dim=2 #输出神经元个数 nn_hdim=100 #梯度下降参数 epsilon=0.01 #learning rate reg_lambda=0.01 #正则化长度 #设置共享变量 w1=theano.shared(np.random.randn(nn_input_dim,nn_hdim),name="W1") b1=theano.shared(np.zeros(nn_hdim),name="b1") w2=theano.shared(np.random.randn(nn_hdim,nn_output_dim),name="W2") b2=theano.shared(np.zeros(nn_output_dim),name="b2") #前馈算法 X=T.matrix('X') #double类型的矩阵 y=T.lvector('y') #int64类型的向量 z1=X.dot(w1)+b1 a1=T.tanh(z1) z2=a1.dot(w2)+b2 y_hat=T.nnet.softmax(z2) #正则化项 loss_reg=1./num_example * reg_lambda/2 * (T.sum(T.square(w1))+T.sum(T.square(w2))) loss=T.nnet.categorical_crossentropy(y_hat,y).mean()+loss_reg #预测结果 prediction=T.argmax(y_hat,axis=1) forword_prop=theano.function([X],y_hat) calculate_loss=theano.function([X,y],loss) predict=theano.function([X],prediction) #求导 dw2=T.grad(loss,w2) db2=T.grad(loss,b2) dw1=T.grad(loss,w1) db1=T.grad(loss,b1) #更新值 gradient_step=theano.function( [X,y], updates=( (w2,w2-epsilon*dw2), (b2,b2-epsilon*db2), (w1,w1-epsilon*dw1), (b1,b1-epsilon*db1) ) ) def build_model(num_passes=20000,print_loss=False): w1.set_value(np.random.randn(nn_input_dim, nn_hdim) / np.sqrt(nn_input_dim)) b1.set_value(np.zeros(nn_hdim)) w2.set_value(np.random.randn(nn_hdim, nn_output_dim) / np.sqrt(nn_hdim)) b2.set_value(np.zeros(nn_output_dim)) for i in xrange(0,num_passes): gradient_step(train_X,train_y) if print_loss and i%1000==0: print "Loss after iteration %i: %f" %(i,calculate_loss(train_X,train_y)) def accuracy_rate(): predict_result=predict(train_X) count=0; for i in range(len(predict_result)): realResult=train_y[i] if(realResult==predict_result[i]): count+=1 print "the correct rate is :%f" %(float(count)/len(predict_result)) def plot_decision_boundary(pred_func): # Set min and max values and give it some padding x_min, x_max = train_X[:, 0].min() - .5, train_X[:, 0].max() + .5 y_min, y_max = train_X[:, 1].min() - .5, train_X[:, 1].max() + .5 h = 0.01 # Generate a grid of points with distance h between them xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Predict the function value for the whole gid Z = pred_func(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) # Plot the contour and training examples plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) plt.scatter(train_X[:, 0], train_X[:, 1], c=train_y, cmap=plt.cm.Spectral) plt.show() build_model(print_loss=True) accuracy_rate() # # plot_decision_boundary(lambda x: predict(x)) # # plt.title("Decision Boundary for hidden layer size 3")
# -*- coding: utf-8 -*- import theano import theano.tensor as T import numpy as np from sklearn import datasets import matplotlib.pyplot as plt import time #定义数据类型 np.random.seed(0) train_X, train_y = datasets.make_moons(5000, noise=0.20) train_y_onehot = np.eye(2)[train_y] #设置参数 num_example=len(train_X) nn_input_dim=2 #输入神经元个数 nn_output_dim=2 #输出神经元个数 nn_hdim=1000 #梯度下降参数 epsilon=np.float32(0.01) #learning rate reg_lambda=np.float32(0.01) #正则化长度 #设置共享变量 # GPU NOTE: Conversion to float32 to store them on the GPU! X = theano.shared(train_X.astype('float32')) # initialized on the GPU y = theano.shared(train_y_onehot.astype('float32')) # GPU NOTE: Conversion to float32 to store them on the GPU! w1 = theano.shared(np.random.randn(nn_input_dim, nn_hdim).astype('float32'), name='W1') b1 = theano.shared(np.zeros(nn_hdim).astype('float32'), name='b1') w2 = theano.shared(np.random.randn(nn_hdim, nn_output_dim).astype('float32'), name='W2') b2 = theano.shared(np.zeros(nn_output_dim).astype('float32'), name='b2') #前馈算法 z1=X.dot(w1)+b1 a1=T.tanh(z1) z2=a1.dot(w2)+b2 y_hat=T.nnet.softmax(z2) #正则化项 loss_reg=1./num_example * reg_lambda/2 * (T.sum(T.square(w1))+T.sum(T.square(w2))) loss=T.nnet.categorical_crossentropy(y_hat,y).mean()+loss_reg #预测结果 prediction=T.argmax(y_hat,axis=1) forword_prop=theano.function([],y_hat) calculate_loss=theano.function([],loss) predict=theano.function([],prediction) #求导 dw2=T.grad(loss,w2) db2=T.grad(loss,b2) dw1=T.grad(loss,w1) db1=T.grad(loss,b1) #更新值 gradient_step=theano.function( [], updates=( (w2,w2-epsilon*dw2), (b2,b2-epsilon*db2), (w1,w1-epsilon*dw1), (b1,b1-epsilon*db1) ) ) def build_model(num_passes=20000,print_loss=False): w1.set_value((np.random.randn(nn_input_dim, nn_hdim) / np.sqrt(nn_input_dim)).astype('float32')) b1.set_value(np.zeros(nn_hdim).astype('float32')) w2.set_value((np.random.randn(nn_hdim, nn_output_dim) / np.sqrt(nn_hdim)).astype('float32')) b2.set_value(np.zeros(nn_output_dim).astype('float32')) for i in xrange(0,num_passes): start=time.time() gradient_step() end=time.time() # print "time require:" # print(end-start) if print_loss and i%1000==0: print "Loss after iteration %i: %f" %(i,calculate_loss()) def accuracy_rate(): predict_result=predict() count=0; for i in range(len(predict_result)): realResult=train_y[i] if(realResult==predict_result[i]): count+=1 print "count" print count print "the correct rate is :%f" %(float(count)/len(predict_result)) def plot_decision_boundary(pred_func): # Set min and max values and give it some padding x_min, x_max = train_X[:, 0].min() - .5, train_X[:, 0].max() + .5 y_min, y_max = train_X[:, 1].min() - .5, train_X[:, 1].max() + .5 h = 0.01 # Generate a grid of points with distance h between them xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Predict the function value for the whole gid Z = pred_func(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) # Plot the contour and training examples plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) plt.scatter(train_X[:, 0], train_X[:, 1], c=train_y, cmap=plt.cm.Spectral) plt.show() build_model(print_loss=True) accuracy_rate() # plot_decision_boundary(lambda x: predict(x)) # plt.title("Decision Boundary for hidden layer size 3")