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import torch
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from model.model import ExcitometerModel
from data.dataset import load_dataset, preprocess_data # Assuming you have these functions
from data.preprocess import preprocess_data
# Configuration
batch_size = 32
learning_rate = 0.001
num_epochs = 10
num_classes = 10 # Adjust based on your specific use case
# Initialize the model
model = ExcitometerModel(num_classes=num_classes)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
# Loss function and optimizer
criterion = torch.nn.CrossEntropyLoss() # Assuming a classification problem
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Load data
def load_data():
# Load and preprocess dataset
train_data = load_dataset('train') # Replace with actual dataset loading
val_data = load_dataset('val') # Replace with actual dataset loading
train_dataset = Dataset(train_data, preprocess_data) # Define Dataset class
val_dataset = Dataset(val_data, preprocess_data) # Define Dataset class
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
return train_loader, val_loader
train_loader, val_loader = load_data()
# Training loop
def train_epoch():
model.train()
running_loss = 0.0
correct = 0
total = 0
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
_, predicted = torch.max(outputs, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
epoch_loss = running_loss / len(train_loader.dataset)
epoch_acc = correct / total
return epoch_loss, epoch_acc
# Validation loop
def validate_epoch():
model.eval()
running_loss = 0.0
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in val_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
running_loss += loss.item() * inputs.size(0)
_, predicted = torch.max(outputs, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
epoch_loss = running_loss / len(val_loader.dataset)
epoch_acc = correct / total
return epoch_loss, epoch_acc
# Training and validation
for epoch in range(num_epochs):
train_loss, train_acc = train_epoch()
val_loss, val_acc = validate_epoch()
print(f'Epoch [{epoch+1}/{num_epochs}]')
print(f'Train Loss: {train_loss:.4f}, Train Accuracy: {train_acc:.4f}')
print(f'Val Loss: {val_loss:.4f}, Val Accuracy: {val_acc:.4f}')
# Save the model
torch.save(model.state_dict(), 'excitometer_model.pth')
print('Training complete. Model saved as excitometer_model.pth')
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