手写数字

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import matplotlib.pyplot as plt

transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])

train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transform, download=True)

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)

class SimpleCNN(nn.Module):
def init(self):
super(SimpleCNN, self).init()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(64 * 7 * 7, 128)
self.fc2 = nn.Linear(128, 10)

def forward(self, x):
    x = F.relu(self.conv1(x))
    x = F.max_pool2d(x, 2)
    x = F.relu(self.conv2(x))
    x = F.max_pool2d(x, 2)
    x = x.view(-1, 64 * 7 * 7)
    x = F.relu(self.fc1(x))
    x = self.fc2(x)
    return x

model = SimpleCNN()

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

num_epochs = 5
model.train()

for epoch in range(num_epochs):
total_loss = 0
for images, labels in train_loader:
outputs = model(images)
loss = criterion(outputs, labels)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    total_loss += loss.item()

print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {total_loss / len(train_loader):.4f}")

model.eval()
correct = 0
total = 0

with torch.no_grad():
for images, labels in test_loader:
outputs = model(images)
_, predicted = torch.max(outputs, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()

accuracy = 100 * correct / total
print(f"Test Accuracy: {accuracy:.2f}%")

dataiter = iter(test_loader)
images, labels = next(dataiter)
outputs = model(images)
_, predictions = torch.max(outputs, 1)

fig, axes = plt.subplots(1, 6, figsize=(12, 4))
for i in range(6):
axes[i].imshow(images[i][0], cmap='gray')
axes[i].set_title(f"Label: {labels[i]}\nPred: {predictions[i]}")
axes[i].axis('off')
plt.show()