Simplified output

scripts/model_exploration.py

@@ -82,34 +82,19 @@
 #     model = TokenLevelModel(num_class=dataset.num_class, device=device).to(device)
 
 
-lr = 1e-2  # 1e-3
+lr = 1e-3  # 1e-3
 optimizer = AdamW(model.parameters(), lr=lr)
-scheduler = get_linear_schedule_with_warmup(optimizer,
-                                            num_warmup_steps=5,
-                                            num_training_steps=num_training_steps)
+# scheduler = get_linear_schedule_with_warmup(optimizer,
+#                                             num_warmup_steps=5,
+#                                             num_training_steps=num_training_steps)
 val_losses = []
 batches_losses = []
 val_acc = []
 avg_running_time = []
 for epoch in range(EPOCH):
 
     t0 = time.time()
-    print(f"\n=============== EPOCH {epoch+1} / {EPOCH} ===============\n")
     batches_losses_tmp = train_loop(train_data_loader, model, optimizer, device, overlap_len)
     epoch_loss = np.mean(batches_losses_tmp)
-    print("\n ******** Running time this step..", time.time() - t0)
-    avg_running_time.append(time.time() - t0)
-    print(f"\n*** avg_loss : {epoch_loss:.2f}, time : ~{(time.time()-t0)//60} min ({time.time()-t0:.2f} sec) ***\n")
-    t1 = time.time()
-    eval_token_classification(valid_data_loader, model, device, overlap_len, num_labels)
-    # output, target, val_losses_tmp = eval_loop_fun1(valid_data_loader, model, device)
-    # print(f"==> evaluation : avg_loss = {np.mean(val_losses_tmp):.2f}, time : {time.time()-t1:.2f} sec\n")
-    # tmp_evaluate = evaluate(target.reshape(-1), output)
-    # print(f"=====>\t{tmp_evaluate}")
-    # val_acc.append(tmp_evaluate['accuracy'])
-    # val_losses.append(val_losses_tmp)
-    # batches_losses.append(batches_losses_tmp)
-    # print("\t§§ model has been saved §§")
-
-# print("\n\n$$$$ average running time per epoch (sec)..", sum(avg_running_time)/len(avg_running_time))
-# # torch.save(model, "models/"+model_dir+"/model_epoch{epoch+1}.pt")
+    print(f"Epoch {epoch} average loss: {epoch_loss} ({time.time() - t0} sec)")
+    eval_token_classification(valid_data_loader, model, device, overlap_len, num_labels)