第一步、匯入需要的包

import os
import scipy.io as sio
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from torch.autograd import Variable

batchSize = 128 # batchsize的大小
niter = 10      # epoch的最大值  
 

第二步、構建神經網路

設神經網路為如上圖所示，輸入層4個節點，兩層隱含層各4個節點，輸出層一個節點。每一層網路所做的都是線性變換，即y=W×X+b；程式碼實現如下：

class Neuralnetwork(nn.Module):
    def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim):
        super(Neuralnetwork, self).__init__()
        self.layer1 = nn.Linear(in_dim, n_hidden_1)
        self.layer2 = nn.Linear(n_hidden_1, n_hidden_2)
        self.layer3 = nn.Linear(n_hidden_2, out_dim)
 
    def forward(self, x):
        x = x.view(x.size(0), -1)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        return x
 
model = Neuralnetwork(1*3, 4, 4, 1)

print(model)  # net architecture
 

Neuralnetwork(
 (layer1): Linear(in_features=3, out_features=4, bias=True)
 (layer2): Linear(in_features=4, out_features=4, bias=True)
 (layer3): Linear(in_features=4, out_features=1, bias=True)
)​​

第三步、讀取資料

自定義的資料為demo_SBPFea.mat，是MATLAB儲存的資料格式，其儲存的內容如下：包括fea(1000*3)和sbp(1000*1)兩個陣列；fea為特徵向量，行為樣本數，列為特徵寬度；sbp為標籤

class SBPEstimateDataset(Dataset):
  
    def __init__(self, ext='demo'):
    
        data = sio.loadmat(ext+'_SBPFea.mat')
        self.fea = data['fea']
        self.sbp = data['sbp']
        
    def __len__(self):
        
        return len(self.sbp)

    def __getitem__(self, idx):

        fea = self.fea[idx]
        sbp = self.sbp[idx]
        """Convert ndarrays to Tensors."""
        return {'fea': torch.from_numpy(fea).float(),
                'sbp': torch.from_numpy(sbp).float()
               }
        
train_dataset = SBPEstimateDataset(ext='demo')
train_loader = DataLoader(train_dataset, batch_size=batchSize, # 分批次訓練
                          shuffle=True,  num_workers=int(8))

整個資料樣本為1000，以batchSize = 128劃分，分為8份，前7份為104個樣本，第8份則為104個樣本。在網路訓練過程中，是一份資料一份資料進行訓練的

第四步、模型訓練

# 優化器，Adam 
optimizer = optim.Adam(list(model.parameters()), lr=0.0001, betas=(0.9, 0.999),weight_decay=0.004) 
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.997) 
criterion = nn.MSELoss() # loss function 

if torch.cuda.is_available(): # 有GPU，則用GPU計算
     model.cuda() 
     criterion.cuda() 
 
for epoch in range(niter): 
     losses = [] 
     ERROR_Train = [] 
     model.train() 
     for i, data in enumerate(train_loader, 0): 
         model.zero_grad()# 首先提取清零 
         real_cpu, label_cpu = data['fea'], data['sbp'] 
 
         if torch.cuda.is_available():# CUDA可用情況下，將Tensor 在GPU上執行 
             real_cpu = real_cpu.cuda() 
             label_cpu = label_cpu.cuda() 
 
 
             input=real_cpu 
             label=label_cpu 
 
             inputv = Variable(input) 
             labelv = Variable(label) 
 
             output = model(inputv) 
             err = criterion(output, labelv) 
             err.backward() 
             optimizer.step() 
 
             losses.append(err.data[0]) 
 
             error = output.data-label+ 1e-12 
             ERROR_Train.extend(error) 
 
     MAE = np.average(np.abs(np.array(ERROR_Train))) 
     ME = np.average(np.array(ERROR_Train)) 
     STD = np.std(np.array(ERROR_Train)) 
 
     print('[%d/%d] Loss: %.4f MAE: %.4f Mean Error: %.4f STD: %.4f' % ( 
     epoch, niter, np.average(losses), MAE, ME, STD))​​

[0/10] Loss: 18384.6699 MAE: 135.3871 Mean Error: -135.3871 STD: 7.5580
[1/10] Loss: 17063.0215 MAE: 130.4145 Mean Error: -130.4145 STD: 7.8918
[2/10] Loss: 13689.1934 MAE: 116.6625 Mean Error: -116.6625 STD: 9.7946
[3/10] Loss: 8192.9053 MAE: 89.6611 Mean Error: -89.6611 STD: 12.9911
[4/10] Loss: 2979.1340 MAE: 52.5410 Mean Error: -52.5279 STD: 15.0930
[5/10] Loss: 599.7094 MAE: 22.2735 Mean Error: -19.9979 STD: 14.2069
[6/10] Loss: 207.2831 MAE: 11.2394 Mean Error: -4.8821 STD: 13.5528
[7/10] Loss: 189.8173 MAE: 9.8020 Mean Error: -1.2357 STD: 13.7095
[8/10] Loss: 188.3376 MAE: 9.6512 Mean Error: -0.6498 STD: 13.7075
[9/10] Loss: 186.8393 MAE: 9.6946 Mean Error: -1.0850 STD: 13.6332​