سلسلة الذكاء الاصطناعي والاستشعار عن بعد – الجزء الثاني

				
					import copy

plot_losses = []
scheduler_counter = 0
best_model = None

for epoch in range(N_EPOCHS):
  # training
  model.train()
  loss_list = []
  acc_list = []
  for batch_i, (x, y) in enumerate(floodnet_dataloader_train):
      pred_mask = model(x.to(device).float())
      loss = criterion(pred_mask, y.to(device).type(torch.int64))

      optimizer.zero_grad()
      loss.backward()
      optimizer.step()
      loss_list.append(loss.cpu().detach().numpy())
      acc_list.append(acc(y,pred_mask).numpy())

      print(
          "\r[Epoch %d/%d] [Batch %d/%d] [Loss: %f (%f)]"
          % (
              epoch,
              N_EPOCHS,
              batch_i,
              len(floodnet_dataloader_train),
              loss.cpu().detach().numpy(),
              np.mean(loss_list),
          )
      )
  scheduler_counter += 1

  # testing
  model.eval()
  val_loss_list = []
  val_acc_list = []
  for batch_i, (x, y) in enumerate(floodnet_dataloader_test):
      with torch.no_grad():
          pred_mask = model(x.to(device).float())
      val_loss = criterion(pred_mask, y.to(device).type(torch.int64))
      val_loss_list.append(val_loss.cpu().detach().numpy())
      val_acc_list.append(acc(y,pred_mask).numpy())

  print(' epoch {} - loss : {:.5f} - acc : {:.2f} - val loss : {:.5f} - val acc : {:.2f}'.format(epoch,
                                                                                                  np.mean(loss_list),
                                                                                                  np.mean(acc_list),
                                                                                                  np.mean(val_loss_list),
                                                                                                  np.mean(val_acc_list)))
  plot_losses.append([epoch, np.mean(loss_list), np.mean(val_loss_list)])

  compare_loss = np.mean(val_loss_list)
  is_best = compare_loss < min_loss
  if is_best == True:
    scheduler_counter = 0
    min_loss = min(compare_loss, min_loss)
    torch.save(model.state_dict(), './saved_models/unet_epoch_{}_{:.5f}.pt'.format(epoch,np.mean(val_loss_list)))
    best_model = copy.deepcopy(model)

  if scheduler_counter > 5:
      lr_scheduler.step()
      print(f"lowering learning rate to {optimizer.param_groups[0]['lr']}")
      scheduler_counter = 0