import pkbar
import time

pbar = pkbar.Pbar(name='loading and processing dataset', target=10)

for i in range(10):
    time.sleep(0.1)
    pbar.update(i)

import pkbar
import torch

# training loop
train_per_epoch = num_of_batches_per_epoch

for epoch in range(num_epochs):

    print('Epoch: %d/%d' % (epoch + 1, num_epochs))
    kbar = pkbar.Kbar(target=train_per_epoch, width=8)
    
    # training
    for i in range(train_per_epoch):
        outputs = model(inputs)
        train_loss = criterion(outputs, targets)
        train_rmse = torch.sqrt(train_loss)
        optimizer.zero_grad()
        train_loss.backward()
        optimizer.step()

        kbar.update(i, values=[("loss", train_loss), ("rmse", train_rmse)])

    # validation
    outputs = model(inputs)
    val_loss = criterion(outputs, targets)
    val_rmse = torch.sqrt(val_loss)

    kbar.add(1, values=[("loss", train_loss), ("rmse", train_rmse),
                        ("val_loss", val_loss), ("val_rmse", val_rmse)])