torch101_validation.py

losses = []
val_losses = []
train_step = make_train_step(model, loss_fn, optimizer)

for epoch in range(n_epochs):
    for x_batch, y_batch in train_loader:
        x_batch = x_batch.to(device)
        y_batch = y_batch.to(device)

        loss = train_step(x_batch, y_batch)
        losses.append(loss)
        
    with torch.no_grad():
        for x_val, y_val in val_loader:
            x_val = x_val.to(device)
            y_val = y_val.to(device)
            
            model.eval()

            yhat = model(x_val)
            val_loss = loss_fn(y_val, yhat)
            val_losses.append(val_loss.item())

print(model.state_dict())

a few improvements:

1. model.eval() is best to be placed before: for x_val, y_val in val_loader - why re-run it in the loop?
2. you're missing model.train(), so on epoch 2 and onward you will run in eval mode when you want train mode - i.e. no dropout, etc.