Aktualisieren main_CrossEntropy.py

main_CrossEntropy.py

+import torch
+from torch import nn
+import numpy as np
+from structures import *
+from models import *
 
+dataset = torch.load("dataset.data")
+
+# hyperparameters
+epochs=2
+batch_size=20
+learning_rate=0.001
+
+train_dataloader = torch.utils.data.DataLoader(dataset.train, batch_size=batch_size, shuffle=True, collate_fn=lambda x:x)
+
+m = SummarisationModelWithCrossEntropyLoss()
+
+since = time.time()
+val_rouge_history = []
+
+best_rouge = 0.0
+best_model_wts = copy.deepcopy(m.state_dict())
+
+for epoch in range(epochs):
+    
+    print()
+    print('Epoch {}/{}'.format(epoch+1, epochs))
+    print('-' * 10)
+
+    # train phase
+    epoch_loss = m.training_epoch(train_dataloader)
+    print('Train Loss: {:.4f}'.format(epoch_loss))
+    
+    # validation phase 
+    epoch_rouge = m.validation(dataset.validation)
+    val_rouge_history.append(epoch_rouge)    
+    print('Validation Rouge Score: {:.4f}'.format(epoch_rouge))
+
+    # epoch completed, deep copy the best model sofar
+    if epoch_rouge > best_rouge:
+        best_rouge = epoch_rouge
+        best_model_wts = copy.deepcopy(m.state_dict())
+
+# after training completed
+time_elapsed = time.time() - since
+print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
+print('Best val rouge: {:4f}'.format(best_rouge))
+# write val_rouge_history in file
+
+# load best model weights
+m.load_state_dict(best_model_wts)
+
+# testing
+since = time.time()
+
+epoch_rouge_1, epoch_rouge_2, epoch_rouge_l = m.test(dataset.test)
+print('Test rouge_1: {:.4f} rouge_2: {:.4f} rouge_l: {:.4f} mean: {:.4f}'.format(epoch_rouge_1, epoch_rouge_2, epoch_rouge_l, (epoch_rouge_1+epoch_rouge_2+epoch_rouge_l)/3.0))
+
+# after testing completed
+time_elapsed = time.time() - since
+print('Testing complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))