import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms# Define the CNN model
class CNN(nn.Module):def __init__(self):super(CNN, self).__init__()self.conv1 = nn.Conv2d(1, 32, 3, padding=1)self.relu1 = nn.ReLU()self.pool1 = nn.MaxPool2d(2, 2)self.conv2 = nn.Conv2d(32, 64, 3, padding=1)self.relu2 = nn.ReLU()self.pool2 = nn.MaxPool2d(2, 2)self.conv3 = nn.Conv2d(64, 64, 3, padding=1)self.relu3 = nn.ReLU()self.flatten = nn.Flatten()self.fc1 = nn.Linear(64 * 7 * 7, 64)self.relu4 = nn.ReLU()self.fc2 = nn.Linear(64, 10)def forward(self, x):x = self.pool1(self.relu1(self.conv1(x)))x = self.pool2(self.relu2(self.conv2(x)))x = self.relu3(self.conv3(x))x = self.flatten(x)x = self.relu4(self.fc1(x))x = self.fc2(x)return x# Load Fashion MNIST dataset
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
train_dataset = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform)
test_dataset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform)train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)# Initialize the CNN model
model = CNN()# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)# Train the model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)epochs = 5
for epoch in range(epochs):running_loss = 0.0for i, data in enumerate(train_loader, 0):inputs, labels = data[0].to(device), data[1].to(device)optimizer.zero_grad()outputs = model(inputs)loss = criterion(outputs, labels)loss.backward()optimizer.step()running_loss += loss.item()print(f'Epoch {epoch + 1}, Loss: {running_loss / len(train_loader)}')print("Training finished!")# Evaluate the model
correct = 0
total = 0
with torch.no_grad():for data in test_loader:images, labels = data[0].to(device), data[1].to(device)outputs = model(images)_, predicted = torch.max(outputs.data, 1)total += labels.size(0)correct += (predicted == labels).sum().item()print(f"Accuracy on the test set: {100 * correct / total}%")