《模式识别原理、应用及实现》习题答案 第9章

习题9-1：针对一段水管监听的数据，在读取了该特征后，基于LSTM设计模型实现对其管道中流量进行预测。习题9.1答案importtorchimporttorch.nnasnnimporth5pyimportnumpyasnpclassCustomLSTM(nn.Module):

def__init__(self,input_size,hidden_size,num_layers,output_size):

super(CustomLSTM,self).__init__()

self.lstm=nn.LSTM(input_size,hidden_size,num_layers,batch_first=True)

self.fc=nn.Linear(hidden_size,output_size)

defforward(self,x):

h0=torch.zeros(self.num_layers,x.size(0),self.hidden_size).to(x.device)

c0=torch.zeros(self.num_layers,x.size(0),self.hidden_size).to(x.device)

out,_=self.lstm(x,(h0,c0))

out=self.fc(out[:,-1,:])

#Output

returnoutdeftrain_model(model,train_data,num_epochs,learning_rate):

criterion=nn.MSELoss()

optimizer=torch.optim.Adam(model.parameters(),lr=learning_rate)

forepochinrange(num_epochs):

fordataintrain_data:

inputs=torch.tensor(data[:,:-1],dtype=torch.float32)

target=torch.tensor(data[:,-1],dtype=torch.float32)

optimizer.zero_grad()

outputs=model(inputs.unsqueeze(0))

loss=criterion(outputs,target)

loss.backward()

optimizer.step()defpredict(model,input_data):

input_tensor=torch.tensor(input_data,dtype=torch.float32)

output=model(input_tensor.unsqueeze(0))

returnoutput.detach().numpy()defread_hdf5_sensor_data(file_path,flow_val):

f=h5py.File(file_path,"r")

data_DAS=f['DAS'][:]

cut_len=1024

sound_data=data_DAS[:,0]

#sound_data进行分割

sound_data_slice=[sound_data[i:i+cut_len]foriinrange(0,len(sound_data),cut_len)]

#Appendflow_valtoeachchunkasthelastelement

forchunkinsound_data_slice:

chunk.append(flow_val)

returnsound_data_slice#construct_train_data根据文件名和流量，构建数据集defconstruct_train_dataset():

flow_data1=read_hdf5_sensor_data('./0000003093.hdf5',flow_val=264.8)

flow_data2=read_hdf5_sensor_data('./0000003095.hdf5',flow_val=270.7)

flow_data3=read_hdf5_sensor_data('./0000003097.hdf5',flow_val=279.5)

sound_dataset=np.vstack((flow_data1,flow_data2,flow_data3))

returnsound_datasetif__name__=='__main__':

#构建数据集

sound_dataset=construct_train_dataset()

#流量预测准确性阈值

flow_threshold=10

#定义模型的输入和层数以及输出

input_size=1024

hidden_size=64

num_layers=1

output_size=1

model=CustomLSTM(input_size,hidden_size,num_layers,output_size)

#训练模型

num_epochs=100

learning_rate=0.001

train_model(model,sound_dataset,num_epochs,learning_rate)

#保存模型

torch.save(model.state_dict(),'./trained_model.pth')

#验证该模型

total_samples=len(sound_dataset)

misclassification_count=0

fordatainsound_dataset:

inputs=torch.tensor(data[:,:-1],dtype=torch.float32)

target=torch.tensor(data[:,-1],dtype=torch.float32)

output=model(inputs.unsqueeze(0))

ifabs(output.item()-target.item())>flow_threshold:

misclassification_count+=1

misclassification_rate=misclassification_count/total_samples

print(f"误识别率:{misclassification_rate}")习题9.2：试用MTCNN网络对AttributesDataset(CelebA)人脸数据集中的人脸进行定位并显示相应的定位点。参考模型-MTCNN-sample.py参考图像数据库-CelebADataset(.hk)习题9.2答案importmatplotlib.pyplotaspltfrommtcnnimportMTCNNimportcv2importnumpyasnpfromPILimportImage#定义一个函数来绘制检测结果defdraw_faces(image,result_list):data=np.array(image)fori,faceinenumerate(result_list):#获取边界框坐标x,y,w,h=face['box']#绘制边界框cv2.rectangle(data,(x,y),(x+w,y+h),(0,155,255),2)#标记关键点forkeyin['left_eye','right_eye','nose','mouth_left','mouth_right']:cv2.circle(data,(face['keypoints'][key][0],face['keypoints'][key][1]),2,(0,155,255),20)plt.imshow(data)plt.show()#初始化MTCNN模型detector=MTCNN()#加载CelebA数据集中的图像#假设你已经知道CelebA数据集的路径，并且有一个图像的路径image_path=r'E:\14datasets\CelebAMask-HQ\CelebAMask-HQ\CelebA-HQ-img\0.jpg'image=Image.open(image_path)#转换图像为RGB格式image=image.convert('RGB')#将图像转换为numpy数组pixels=np.array(image)#使用MTCNN检测人脸result=detector.detect_faces(pixels)#在图像上绘制检测结果draw_faces(image,result)习题9-3：试用VIT网络对交通道路上的人、车进行识别。模型参考-/google-research/vision_transformer参考数据集-TheUA-DETRACBenchmarkSuite()习题9.3答案importmathfromPILimportImageimportrequestsimportmatplotlib.pyplotaspltimportipywidgetsaswidgetsfromIPython.displayimportdisplay,clear_outputimporttorchfromtorchimportnnfromtorchvision.modelsimportresnet50importtorchvision.transformsasTtorch.set_grad_enabled(False);#COCOclassesCLASSES=['N/A','person','bicycle','car','motorcycle','airplane','bus','train','truck','boat','trafficlight','firehydrant','N/A','stopsign','parkingmeter','bench','bird','cat','dog','horse','sheep','cow','elephant','bear','zebra','giraffe','N/A','backpack','umbrella','N/A','N/A','handbag','tie','suitcase','frisbee','skis','snowboard','sportsball','kite','baseballbat','baseballglove','skateboard','surfboard','tennisracket','bottle','N/A','wineglass','cup','fork','knife','spoon','bowl','banana','apple','sandwich','orange','broccoli','carrot','hotdog','pizza','donut','cake','chair','couch','pottedplant','bed','N/A','diningtable','N/A','N/A','toilet','N/A','tv','laptop','mouse','remote','keyboard','cellphone','microwave','oven','toaster','sink','refrigerator','N/A','book','clock','vase','scissors','teddybear','hairdrier','toothbrush']#colorsforvisualizationCOLORS=[[0.000,0.447,0.741],[0.850,0.325,0.098],[0.929,0.694,0.125],[0.494,0.184,0.556],[0.466,0.674,0.188],[0.301,0.745,0.933]]#standardPyTorchmean-stdinputimagenormalizationtransform=T.Compose([T.Resize(800),T.ToTensor(),T.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])])#foroutputboundingboxpost-processingdefbox_cxcywh_to_xyxy(x):x_c,y_c,w,h=x.unbind(1)b=[(x_c-0.5*w),(y_c-0.5*h),(x_c+0.5*w),(y_c+0.5*h)]returntorch.stack(b,dim=1)defrescale_bboxes(out_bbox,size):img_w,img_h=sizeb=box_cxcywh_to_xyxy(out_bbox)b=b*torch.tensor([img_w,img_h,img_w,img_h],dtype=torch.float32)returnbdefplot_results(pil_img,prob,boxes):plt.figure(figsize=(16,10))plt.imshow(pil_img)ax=plt.gca()colors=COLORS*100forp,(xmin,ymin,xmax,ymax),cinzip(prob,boxes.tolist(),colors):ax.add_patch(plt.Rectangle((xmin,ymin),xmax-xmin,ymax-ymin,fill=False,color=c,linewidth=3))cl=p.argmax()text=f'{CLASSES