import torch
import torch.nn as nn
from torchvision import datasets,transforms
from visdom import Visdom
viz = Visdom()
batch_size=200
learning_rate=0.01
epochs=10
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
class MLP(nn.Module):
def __init__(self):
super(MLP,self).__init__()
self.model = nn.Sequential(
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
device =torch.device('cuda:0')
net = MLP().to(device)
#weight_decay 权重衰减
optimizer = torch.optim.SGD(net.parameters(),lr=learning_rate,weight_decay=0.01)
criteon = torch.nn.CrossEntropyLoss().to(device)
viz = Visdom()
viz.line([0.],[0.],win='train_loss',opts=dict(title='train loss'))
viz.line([[0.0,0.0]],[0.],win='test',opts=dict(title='test loss&acc',
legend=['loss','acc']))
global_step = 0
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.cuda()
logits = net(data)
loss = criteon(logits,target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
global_step +=1
viz.line([loss.item()],[global_step],win='train_loss',update='append')
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()))
test_loss = 0
correct = 0
for data, target in test_loader:
data = data.view(-1, 28 * 28)
data,target = data.to(device),target.cuda()
logits = net(data)
test_loss += criteon(logits, target).item()
pred = logits.data.max(1)[1]
correct += pred.eq(target.data).float().sum().item()
viz.line([[test_loss,correct/len(test_loader.dataset)]],[global_step],win='test',update='append')
# viz.images(data.view(-1, 1, 28, 28), win='x')
# viz.text(str(pred.detach().cpu().numpy()), win='pred',
# opts=dict(title='pred'))
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)))
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