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02e54e3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | import torch
def train(model, train_loader, criterion, optimizer, device):
model.train()
running_loss = 0.0
correct_predictions = 0
total_predictions = 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()
predicted_labels = outputs.argmax(dim=1)
correct_predictions += (predicted_labels == labels).sum().item()
total_predictions += labels.size(0)
train_loss = running_loss / len(train_loader)
train_accuracy = correct_predictions / total_predictions
return train_loss, train_accuracy
def validate(model, test_loader, criterion, device):
model.eval()
running_loss = 0.0
correct_predictions = 0
total_predictions = 0
with torch.inference_mode():
for inputs, labels in test_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
running_loss += loss.item()
predicted_labels = outputs.argmax(dim=1)
correct_predictions += (predicted_labels == labels).sum().item()
total_predictions += labels.size(0)
test_loss = running_loss / len(test_loader)
test_accuracy = correct_predictions / total_predictions
return test_loss, test_accuracy
def predict(model, test_loader, device):
model.eval()
predictions = []
with torch.inference_mode():
for inputs, _ in test_loader:
inputs = inputs.to(device)
outputs = model(inputs)
predicted_labels = outputs.argmax(dim=1)
predictions.extend(predicted_labels.cpu().numpy())
return predictions
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