《人工智能基础及应用（Python 微课版）》程序代码 第6章 深度学习

第6章深度学习【例6.1】importnumpyasnpimporttensorflowastfimportmatplotlibmatplotlib.use("TkAgg")#强制独立窗口，避开PyCharm的bugimportmatplotlib.pyplotasplt#以下全部保持原样TRUE_W,TRUE_B=3.0,2.0x=np.random.uniform(-1,1,size=(200,1)).astype(np.float32)noise=np.random.normal(0,0.1,size=(200,1)).astype(np.float32)y=TRUE_W*x+TRUE_B+noisetrain_ds=tf.data.Dataset.from_tensor_slices((x,y)).shuffle(200).batch(32)model=tf.keras.Sequential([tf.keras.layers.Dense(1,input_shape=(1,))])pile(optimizer=tf.keras.optimizers.SGD(0.1),loss='mse')model.fit(train_ds,epochs=50,verbose=0)learned_w,learned_b=model.get_weights()print("真实w={:.2f}b={:.2f}".format(TRUE_W,TRUE_B))print("学到w={:.2f}b={:.2f}".format(learned_w[0,0],learned_b[0]))plt.scatter(x,y,label='data')x_line=np.linspace(-1,1,100)plt.plot(x_line,TRUE_W*x_line+TRUE_B,'--',label='trueline')plt.plot(x_line,learned_w[0,0]*x_line+learned_b[0],'r',label='learnedline')plt.legend()plt.show()【例6.2】#----------------------------1.环境准备----------------------------importtorchimporttorch.nnasnnimporttorch.optimasoptimimportmatplotlibmatplotlib.use('TkAgg')#关键：避开SciViewimportmatplotlib.pyplotaspltdevice='cuda'iftorch.cuda.is_available()else'cpu'print('device:',device)#----------------------------2.造数据-----------------------------torch.manual_seed(0)X=torch.unsqueeze(torch.linspace(-1,1,200),dim=1).to(device)y=3*X+2+0.3*torch.randn(X.size()).to(device)#----------------------------3.定义模型-----------------------------classLinearNet(nn.Module):def__init__(self):super().__init__()self.fc=nn.Linear(1,1)defforward(self,x):returnself.fc(x)model=LinearNet().to(device)#----------------------------4.损失与优化器-------------------------criterion=nn.MSELoss()optimizer=optim.SGD(model.parameters(),lr=0.1)#----------------------------5.训练-----------------------------loss_list=[]forepochinrange(201):pred=model(X)loss=criterion(pred,y)optimizer.zero_grad()loss.backward()optimizer.step()loss_list.append(loss.item())ifepoch%40==0:print(f'epoch{epoch:3d}|loss{loss.item():.4f}')#----------------------------6.绘图-----------------------------fig,(ax1,ax2)=plt.subplots(1,2,figsize=(10,4))ax1.scatter(X.cpu(),y.cpu(),s=8,label='Noisydata')X_line=torch.linspace(-1,1,100).unsqueeze(1).to(device)y_line=model(X_line).detach().cpu()ax1.plot(X_line.cpu(),y_line,'r',lw=2,label='Model')ax1.set_xlabel('x');ax1.set_ylabel('y');ax1.legend();ax1.set_title('Data&Prediction')ax2.plot(loss_list)ax2.set_xlabel('epoch');ax2.set_ylabel('MSEloss');ax2.set_title('TrainingLoss')ax2.grid(True)plt.tight_layout()plt.show()#----------------------------7.打印参数----------------------w,b=model.parameters()print(f'学得的权重w={w.item():.3f}（真值3）')print(f'学得的偏置b={b.item():.3f}（真值2）')6.9.2PyTorch实现图像分类pipinstalltorchtorchvision-i/simple#!/usr/bin/envpython#cifar10_torch.pyimporttorchimporttorch.nnasnnimporttorch.optimasoptimimporttorchvisionimporttorchvision.transformsasTfromtorch.utils.dataimportDataLoader#1.超参数-------------------------------------------------------------BATCH_SIZE=128EPOCHS=20LR=1e-3DEVICE='cuda'iftorch.cuda.is_available()else'cpu'NUM_CLASSES=10#2.数据---------------------------------------------------------------transform=T.Compose([T.ToTensor(),T.Normalize((0.4914,0.4822,0.4465),(0.2023,0.1994,0.2010))])train_set=torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform)test_set=torchvision.datasets.CIFAR10(root='./data',train=False,download=True,transform=transform)train_loader=DataLoader(train_set,batch_size=BATCH_SIZE,shuffle=True,num_workers=2)test_loader=DataLoader(test_set,batch_size=BATCH_SIZE,shuffle=False,num_workers=2)#3.模型---------------------------------------------------------------classNet(nn.Module):def__init__(self):super().__init__()self.features=nn.Sequential(nn.Conv2d(3,32,3,padding=1),nn.BatchNorm2d(32),nn.ReLU(),nn.Conv2d(32,32,3,padding=1),nn.ReLU(),nn.MaxPool2d(2),nn.Dropout(0.25),nn.Conv2d(32,64,3,padding=1),nn.BatchNorm2d(64),nn.ReLU(),nn.Conv2d(64,64,3,padding=1),nn.ReLU(),nn.MaxPool2d(2),nn.Dropout(0.25),)self.classifier=nn.Sequential(nn.Flatten(),nn.Linear(64*8*8,512),nn.BatchNorm1d(512),nn.ReLU(),nn.Dropout(0.5),nn.Linear(512,NUM_CLASSES))defforward(self,x):x=self.features(x)x=self.classifier(x)returnxnet=Net().to(DEVICE)criterion=nn.CrossEntropyLoss()optimizer=optim.Adam(net.parameters(),lr=LR)#4.训练---------------------------------------------------------------forepochinrange(1,EPOCHS+1):net.train()running_loss,correct,total=0.0,0,0forx,yintrain_loader:x,y=x.to(DEVICE),y.to(DEVICE)optimizer.zero_grad()out=net(x)loss=criterion(out,y)loss.backward()optimizer.step()running_loss+=loss.item()*x.size(0)_,pred=out.max(1)total+=y.size(0)correct+=pred.eq(y).sum().item()train_acc=100.*correct/totalprint(f'Epoch{epoch:02d}loss={running_loss/total:.4f}acc={train_acc:.2f}%')#5.测试---------------------------------------------------------------net.eval()correct=0withtorch.no_grad():forx,yintest_loader:x,y=x.to(DEVICE),y.to(DEVICE)out=net(x)pred=out.argmax(1)correct+=pred.eq(y).sum().item()test_acc=100.*correct/len(test_set)print(f"\nPyTorch测试准确率:{test_acc:.2f}%")6.9.3TensorFlow实现图像分类#!/usr/bin/envpython#cifar10_tf.pyimporttensorflowastffromtensorflow.kerasimportlayers,models#1.超参数-------------------------------------------------------------BATCH_SIZE=128EPOCHS=20LR=1e-3NUM_CLASSES=10#2.数据---------------------------------------------------------------(x_train,y_train),(x_test,y_test)=tf.keras.datasets.cifar10.load_data()#归一化到[0,1]x_train,x_test=x_train.astype("float32")/255.0,x_test.astype("float32")/255.0#one-hot编码y_train=tf.keras.utils.to_categorical(y_train,NUM_CLASSES)y_test=tf.keras.utils.to_categorical(y_test,NUM_CLASSES)#3.模型---------------------------------------------------------------defmake_model():model=models.Sequential([layers.Conv2D(32,3,padding='same',activation='relu',input_shape=(32,32,3)),layers.BatchNormalization(),layers.Conv2D(32,3,padding='same',activation='relu'),layers.MaxPooling2D(),layers.Dropout(0.25),layers.Conv2D(64,3,padding='same',activation='relu'),layers.BatchNormalization(),layers.Conv2D(64,3,padding='same',activation='relu'),