Pytorch中的数据集划分&正则化方法

import  torch
import  torch.nn as nn
import  torch.nn.functional as F
import  torch.optim as optim
from torchvision import datasets, transforms
#超参数
batch_size=200
learning_rate=0.01
epochs=10
#获取训练数据
train_db = datasets.MNIST('../data', train=True, download=True,#train=True则得到的是训练集
 transform=transforms.Compose([  #transform进行数据预处理
  transforms.ToTensor(),#转成Tensor类型的数据
  transforms.Normalize((0.1307,), (0.3081,)) #进行数据标准化(减去均值除以方差)
 ]))
#DataLoader把训练数据分成多个小组，此函数每次抛出一组数据。直至把所有的数据都抛出。就是做一个数据的初始化
train_loader = torch.utils.data.DataLoader(train_db, batch_size=batch_size, shuffle=True)
#获取测试数据
test_db = datasets.MNIST('../data', train=False,
 transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))
 ]))
test_loader = torch.utils.data.DataLoader(test_db, batch_size=batch_size, shuffle=True)
#将训练集拆分成训练集和验证集
print('train:', len(train_db), 'test:', len(test_db))  #train: 60000 test: 10000
train_db, val_db = torch.utils.data.random_split(train_db, [50000, 10000])
print('db1:', len(train_db), 'db2:', len(val_db))#db1: 50000 db2: 10000
train_loader = torch.utils.data.DataLoader(train_db, batch_size=batch_size, shuffle=True)
val_loader = torch.utils.data.DataLoader(val_db, batch_size=batch_size, shuffle=True)
class MLP(nn.Module):
 def __init__(self):
  super(MLP, self).__init__()
  self.model = nn.Sequential(#定义网络的每一层,
nn.Linear(784, 200),
nn.ReLU(inplace=True),
nn.Linear(200, 200),
nn.ReLU(inplace=True),
nn.Linear(200, 10),
nn.ReLU(inplace=True),
  )
 def forward(self, x):
  x = self.model(x)
  return x
net = MLP()
#定义sgd优化器,指明优化参数、学习率，net.parameters()得到这个类所定义的网络的参数[[w1,b1,w2,b2,...]
optimizer = optim.SGD(net.parameters(), lr=learning_rate)
criteon = nn.CrossEntropyLoss()
for epoch in range(epochs):
 for batch_idx, (data, target) in enumerate(train_loader):
  data = data.view(-1, 28*28) #将二维的图片数据摊平[样本数,784]
  logits = net(data) #前向传播
  loss = criteon(logits, target) #nn.CrossEntropyLoss()自带Softmax
  optimizer.zero_grad() #梯度信息清空
  loss.backward() #反向传播获取梯度
  optimizer.step()#优化器更新
  if batch_idx % 100 == 0: #每100个batch输出一次信息
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
 epoch, batch_idx * len(data), len(train_loader.dataset),
  100. * batch_idx / len(train_loader), loss.item()))
 #验证集用来检测训练是否过拟合
 val_loss = 0
 correct = 0
 for data, target in val_loader:
  data = data.view(-1, 28 * 28)
  logits = net(data)
  val_loss += criteon(logits, target).item()
  pred = logits.data.max(dim=1)[1]
  correct += pred.eq(target.data).sum()
 val_loss /= len(val_loader.dataset)
 print('\nVAL set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
  val_loss, correct, len(val_loader.dataset),
  100. * correct / len(val_loader.dataset)))
#测试集用来评估
test_loss = 0
correct = 0 #correct记录正确分类的样本数
for data, target in test_loader:
 data = data.view(-1, 28 * 28)
 logits = net(data)
 test_loss += criteon(logits, target).item()  #其实就是criteon(logits, target)的值，标量
 pred = logits.data.max(dim=1)[1] #也可以写成pred=logits.argmax(dim=1)
 correct += pred.eq(target.data).sum()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
 test_loss, correct, len(test_loader.dataset),
 100. * correct / len(test_loader.dataset)))

optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate, weight_decay=0.01)

optimizer = torch.optim.SGD(model.parameters(), args=lr, momentum=args.momentum, weight_decay=args.weight_decay)

torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10, verbose=False, threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=0, eps=1e-08)

torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=-1)

torch.nn.Dropout(p=dropout_prob)