畳み込みニューラルネットワーク(CNN)

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

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

trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)

class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.fc1 = nn.Linear(32 * 7 * 7, 128)
        self.fc2 = nn.Linear(128, 10)
        

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 32 * 7 * 7)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = SimpleCNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

for epoch in range(2):
    running_loss = 0.0
    for images, labels in trainloader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f'Epoch {epoch + 1}, Loss: {running_loss / len(trainloader):.4f}')

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

print(f'Accuracy of the model on the test images: {100 * correct / total:.2f}%')

$ python3 cnn.py
100%|██████████████████████████████████████████████████████████████████████████████| 9.91M/9.91M [00:04<00:00, 2.06MB/s] 100%|███████████████████████████████████████████████████████████████████████████████| 28.9k/28.9k [00:00<00:00, 136kB/s] 100%|██████████████████████████████████████████████████████████████████████████████| 1.65M/1.65M [00:01<00:00, 1.20MB/s] 100%|██████████████████████████████████████████████████████████████████████████████| 4.54k/4.54k [00:00<00:00, 1.05MB/s] Epoch 1, Loss: 0.1991 Epoch 2, Loss: 0.0537 Accuracy of the model on the test images: 98.68%