Pytorch 神经网络—自定义数据集上实现教程

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 

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)
)

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, # 分批次训练
             shuffle=True, num_workers=int(8))

# 优化器，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，则用GPU计算
   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可用情况下，将Tensor 在GPU上运行 
       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​