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Pytorch 神經(jīng)網(wǎng)絡—自定義數(shù)據(jù)集上實現(xiàn)教程

 更新時間：2020年01月07日 14:09:58   作者：LZDCQU  
今天小編就為大家分享一篇Pytorch 神經(jīng)網(wǎng)絡—自定義數(shù)據(jù)集上實現(xiàn)教程，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧

第一步、導入需要的包

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(gòu)建神經(jīng)網(wǎng)絡

設神經(jīng)網(wǎng)絡為如上圖所示，輸入層4個神經(jīng)元，兩層隱含層各4個神經(jīng)元，輸出層一個神經(jīng)。每一層網(wǎng)絡所做的都是線性變換，即y=W×X+b；代碼實現(xiàn)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、讀取數(shù)據(jù)

自定義的數(shù)據(jù)為demo_SBPFea.mat，是MATLAB保存的數(shù)據(jù)格式，其存儲的內(nèi)容如下：包括fea(1000*3)和sbp(1000*1)兩個數(shù)組；fea為特征向量，行為樣本數(shù)，列為特征寬度；sbp為標簽

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))

整個數(shù)據(jù)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(wǎng)絡訓練過程中，是一份數(shù)據(jù)一份數(shù)據(jù)進行訓練的

第四步、模型訓練

# 優(yōu)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上運行 
       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​

以上這篇Pytorch 神經(jīng)網(wǎng)絡—自定義數(shù)據(jù)集上實現(xiàn)教程就是小編分享給大家的全部內(nèi)容了，希望能給大家一個參考，也希望大家多多支持腳本之家。

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