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import torch
from typing import Tuple,Dict, List
from tqdm.auto import tqdm
def train_step(model: torch.nn.Module,
dataloader: torch.utils.data.DataLoader,
loss_fn: torch.nn.Module,
optimizer: torch.optim.Optimizer,
device: torch.device) -> Tuple[float, float]:
model.train()
train_loss, train_acc = 0, 0
for batch, (X, y) in enumerate(dataloader):
X, y = X.to(device), y.to(device)
y_pred = model(X)
loss = loss_fn(y_pred, y)
train_loss += loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
y_pred_class = torch.argmax(torch.softmax(y_pred, dim=1), dim=1)
train_acc += (y_pred_class == y).sum().item()/len(y_pred)
train_loss = train_loss / len(dataloader)
train_acc = train_acc / len(dataloader)
return train_loss, train_acc
def test_step(model: torch.nn.Module,
dataloader: torch.utils.data.DataLoader,
loss_fn: torch.nn.Module,
device: torch.device) -> Tuple[float, float]:
model.eval()
test_loss, test_acc = 0, 0
with torch.inference_mode():
for batch, (X, y) in enumerate(dataloader):
X, y = X.to(device), y.to(device)
test_pred_logits = model(X)
loss = loss_fn(test_pred_logits, y)
test_loss += loss.item()
test_pred_labels = test_pred_logits.argmax(dim=1)
test_acc += ((test_pred_labels == y).sum().item()/len(test_pred_labels))
test_loss = test_loss / len(dataloader)
test_acc = test_acc / len(dataloader)
return test_loss, test_acc
def train(model: torch.nn.Module,
train_dataloader: torch.utils.data.DataLoader,
test_dataloader: torch.utils.data.DataLoader,
optimizer: torch.optim.Optimizer,
loss_fn: torch.nn.Module,
epochs: int,
device: torch.device) -> Dict[str, List]:
results = {
"train_loss": [],
"train_acc": [],
"test_loss": [],
"test_acc": []
}
model.to(device)
for epoch in tqdm(range(epochs)):
train_loss, train_acc = train_step(model=model,
dataloader=train_dataloader,
loss_fn=loss_fn,
optimizer=optimizer,
device=device)
test_loss, test_acc = test_step(model=model,
dataloader=test_dataloader,
loss_fn=loss_fn,
device=device)
print(
f"Epoch: {epoch+1} | "
f"train_loss: {train_loss:.4f} | "
f"train_acc: {train_acc:.4f} | "
f"test_loss: {test_loss:.4f} | "
f"test_acc: {test_acc:.4f}"
)
results["train_loss"].append(train_loss)
results["train_acc"].append(train_acc)
results["test_loss"].append(test_loss)
results["test_acc"].append(test_acc)
return results |