Deep Learning with PyTorch

Training the model

We train the model for multiple epochs and validate it using the following code:

# Loop over epochs.best_val_loss = Noneepochs = 40for epoch in range(1, epochs+1):    epoch_start_time = time.time()    trainf()    val_loss = evaluate(valid_iter)    print('-' * 89)    print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '        'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),                                   val_loss, math.exp(val_loss)))    print('-' * 89)    if not best_val_loss or val_loss < best_val_loss:        best_val_loss = val_loss    else:        # Anneal the learning rate if no improvement has been seen in the validation dataset.        lr /= 4.0

The previous code is training the model for 40 epochs, and we start with a high-learning rate of 20 and reduce it further ...