valid.py

def evaluate(model, iterator, criterion):
    
    #initialize every epoch
    epoch_loss = 0
    epoch_acc = 0

    #deactivating dropout layers
    model.eval()
    
    #deactivates autograd
    with torch.no_grad():
    
        for batch in iterator:
        
            #retrieve text and no. of words
            text, text_lengths = batch.text
            
            #convert to 1d tensor
            predictions = model(text, text_lengths).squeeze()
            
            #compute loss and accuracy
            loss = criterion(predictions, batch.label)
            acc = binary_accuracy(predictions, batch.label)
            
            #keep track of loss and accuracy
            epoch_loss += loss.item()
            epoch_acc += acc.item()
        
    return epoch_loss / len(iterator), epoch_acc / len(iterator)