示例#1
0
def train():
    """
    training
    """
    model.train()
    epoch_loss, t0 = [], time.time()

    training_data_loader = DataLoader(
        training_set, batch_size=batch_size, num_workers=2, pin_memory=cuda,
        sampler=SubsetSampler(indices=sample_indecies))

    for i_batch, (indexs, (data, targetY, targetX)) in enumerate(training_data_loader, 1):
        data, targetY, targetX = Variable(data), Variable(targetY), Variable(targetX)
        if cuda:
            data = data.cuda(async=True)
            targetY = targetY.cuda(async=True)
            targetX = targetX.cuda(async=True)

        optimizer.zero_grad()
        mask = model(data)  # prediction
        loss = criterion(apply_mask(mask, targetY), targetX)
        epoch_loss.append(loss.data[0])
        loss.backward()
        optimizer.step()
        print("===> Epoch {:2} {:4.1f}% Loss: {:.4e}".format(
            epoch, i_batch / batch_per_epoch * 100, loss.data[0]))

    # assume loss is emperical mean of the batch and i.i.d
    loss, loss_std, t = np.mean(epoch_loss), np.std(epoch_loss) * batch_size**.5, int(time.time() - t0)
    print("Epoch {} Complete: Avg. Loss: {:.4e} {:.4e} {}".format(epoch, loss, loss_std, int(t / 60)))
    print(epoch, loss, loss_std, t,
          sep=',', end=',', file=open(logpath, 'a'))
示例#2
0
def clean_sample_(model, mask, Y_m, Y_a, length, save_path):
    Xh_m = apply_mask(mask, Y_m)
    y = model.inverse_transform(Xh_m, Y_a, new_length=length)
    save_audio_(y, save_path)