目录
一.过拟合
1.欠拟合,过拟合
how to detect overfitting
二.Train-Val-Test
1.划分Train-Val-Test
2.实现代码
3.交叉验证,k-fold cross validation
Estimated Estimated>Groud-truth,Underfitting,Overfitting (how to detect overfitting) train test trade-off 将train数据集分N份,N-1/N作为train test,1/N作为val test
how to detect overfitting
二.Train-Val-Test
1.划分Train-Val-Test
2.实现代码
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets,transforms
batch_size=200
learning_rate=0.001
epochs=10
#加载数据
train_db = datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
]))
train_loader = torch.utils.data.DataLoader(
train_db,
batch_size=batch_size,shuffle = True
)
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
)
print('train:', len(train_db), 'test:', len(test_db))
#把train_db分为train_db, val_db
train_db, val_db = torch.utils.data.random_split(train_db, [50000, 10000])
print('db1:', len(train_db), 'db2:', len(val_db))
#重新分配下载train_loader,val_loader;之前已分配下载好test_loader
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)
#MLP的model
class MLP(nn.Module):
def __init__(self):
super(MLP,self).__init__()
self.model = nn.Sequential(
nn.Linear(784,200),
nn.LeakyReLU(inplace=True),
nn.Linear(200,200),
nn.LeakyReLU(inplace=True),
nn.Linear(200,10),
nn.LeakyReLU(inplace=True),
)
def forward(self,x):
x = self.model(x)
return x
device = torch.device('cuda:0')
net = MLP().to(device)
optimizer = optim.SGD(net.parameters(),lr = learning_rate)
criteon = nn.CrossEntropyLoss().to(device)
'''net = MLP()
optimizer = optim.SGD(net.parameters(),lr=learning_rate)
criteon = nn.CrossEntropyLoss()'''
#train
for epoch in range(epochs):
for batch_idx,(data,target) in enumerate(train_loader):
data = data.view(-1,28*28)
data, target = data.to(device),target.to(device)#GPU加速时补
logits = net(data)
loss = criteon(logits,target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print('Train Epoch : {} [{}/{}({:.0f}%)]\tLoss:{:.6f}'.format(epoch,batch_idx*len(data),len(train_loader.dataset),100. * batch_idx / len(train_loader),loss.item()))
#validation
test_loss = 0
correct = 0
for data, target in val_loader:
data = data.view(-1, 28 * 28)
data, target = data.to(device), target.to(device)
logits = net(data)
test_loss += criteon(logits, target).item()
pred = logits.data.max(1)[1]
correct += pred.eq(target.data).sum()
test_loss /= len(val_loader.dataset)
print('\nVAL set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(val_loader.dataset),
100. * correct / len(val_loader.dataset)))
#人为监视,选择最好的参数,此处加载最好的参数,使用test_loader数据集做test
#test
test_loss = 0
correct = 0
for data, target in test_loader:
data = data.view(-1,28*28)
data, target = data.to(device),target.to(device)#GPU加速时补
logits = net(data)
test_loss += criteon(logits,target).item()
pred = logits.data.max(1)[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)))
3.交叉验证,k-fold cross validation