1.源码实现
import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdm
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.pool1 = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.pool2 = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(16*4*4, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool1(torch.relu(self.conv1(x)))
x = self.pool2(torch.relu(self.conv2(x)))
x = x.view(-1, 16*4*4)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
tran_dataset = datasets.MNIST('mnist/', download=False, train=True, transform=transform)
test_dataset = datasets.MNIST('mnist/', download=False, train=False, transform=transform)
train_dataloader = DataLoader(tran_dataset, batch_size=256, shuffle=True)
test_dataloader = DataLoader(test_dataset, batch_size=256, shuffle=False)
device = "cpu"
lenet = LeNet().to(device)
epochs = 1000
lr = 1e-4
optimizer = torch.optim.Adam(lenet.parameters(), lr=lr)
loss_fn = nn.CrossEntropyLoss()
train_acc_list = []
test_acc_list = []
train_loss_list = []
for epoch in range(epochs):
train_loss_epoch = []
acc = 0
loss = 1e-4
for train_data, labels in tqdm(train_dataloader):
train_data = train_data.to(device)
labels = labels.to(device)
y_hat = lenet(train_data)
train_loss = loss_fn(y_hat, labels)
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
train_loss_epoch.append(train_loss.cpu().detach().numpy())
right = torch.argmax(y_hat, 1) == labels
acc += right.sum().cpu().detach().numpy()
acc = acc / len(tran_dataset)
train_acc_list.append(acc)
real_loss = sum(train_loss_epoch) / len(train_loss_epoch)
train_loss_list.append(sum(train_loss_epoch) / len(train_loss_epoch))
print(f'epoch:{epoch}, train_loss:{sum(train_loss_epoch) / len(train_loss_epoch)}')
if real_loss < loss:
break;
torch.save(lenet.state_dict(), "mnist.pth")
2.运行程序
$ python train.py
3.结果
运行后将得到保存后的模型文件mnist.pth
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