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'))
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)