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import torch
import torch.optim as optim
import torch.nn as nn
def train_model(model, trainloader, testloader, epochs=10, learning_rate=0.001):
"""
Trains the PyTorch model.
"""
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"Training on device: {device}")
model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
history = {'accuracy': [], 'loss': []}
for epoch in range(epochs):
running_loss = 0.0
correct = 0
total = 0
model.train()
for i, data in enumerate(trainloader, 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()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
epoch_loss = running_loss / len(trainloader)
epoch_acc = correct / total
history['loss'].append(epoch_loss)
history['accuracy'].append(epoch_acc)
print(f'Epoch {epoch + 1}/{epochs} - Loss: {epoch_loss:.4f} - Accuracy: {epoch_acc:.4f}')
print('Finished Training')
return history
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