Merge pull request #25 from DingKe/master

use buit-in detach

tutorials/08 - Language Model/main-gpu.py

@@ -61,7 +61,7 @@ optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 
 # Truncated Backpropagation 
 def detach(states):
-    return [Variable(state.data) for state in states] 
+    return [state.detach() for state in states] 
 
 # Training
 for epoch in range(num_epochs):
@@ -119,4 +119,4 @@ with open(sample_path, 'w') as f:
             print('Sampled [%d/%d] words and save to %s'%(i+1, num_samples, sample_path))
 
 # Save the Trained Model
-torch.save(model.state_dict(), 'model.pkl')
+torch.save(model.state_dict(), 'model.pkl')

tutorials/08 - Language Model/main.py

@@ -61,7 +61,7 @@ optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 
 # Truncated Backpropagation 
 def detach(states):
-    return [Variable(state.data) for state in states] 
+    return [state.detach() for state in states] 
 
 # Training
 for epoch in range(num_epochs):
@@ -119,4 +119,4 @@ with open(sample_path, 'w') as f:
             print('Sampled [%d/%d] words and save to %s'%(i+1, num_samples, sample_path))
 
 # Save the Trained Model
-torch.save(model.state_dict(), 'model.pkl')
+torch.save(model.state_dict(), 'model.pkl')

tutorials/10 - Generative Adversarial Network/main-gpu.py

@@ -77,7 +77,7 @@ for epoch in range(200):
         
         noise = Variable(torch.randn(images.size(0), 128)).cuda()
         fake_images = generator(noise)
-        outputs = discriminator(fake_images) 
+        outputs = discriminator(fake_images.detach()) 
         fake_loss = criterion(outputs, fake_labels)
         fake_score = outputs
         
@@ -107,4 +107,4 @@ for epoch in range(200):
 
 # Save the Models 
 torch.save(generator.state_dict(), './generator.pkl')
-torch.save(discriminator.state_dict(), './discriminator.pkl')
+torch.save(discriminator.state_dict(), './discriminator.pkl')

tutorials/10 - Generative Adversarial Network/main.py

@@ -77,7 +77,7 @@ for epoch in range(200):
         
         noise = Variable(torch.randn(images.size(0), 128))
         fake_images = generator(noise)
-        outputs = discriminator(fake_images) 
+        outputs = discriminator(fake_images.detach()) 
         fake_loss = criterion(outputs, fake_labels)
         fake_score = outputs
         
@@ -107,4 +107,4 @@ for epoch in range(200):
 
 # Save the Models 
 torch.save(generator.state_dict(), './generator.pkl')
-torch.save(discriminator.state_dict(), './discriminator.pkl')
+torch.save(discriminator.state_dict(), './discriminator.pkl')

tutorials/11 - Deep Convolutional Generative Adversarial Network/main-gpu.py

@@ -102,7 +102,7 @@ for epoch in range(50):
         
         noise = Variable(torch.randn(images.size(0), 128)).cuda()
         fake_images = generator(noise)
-        outputs = discriminator(fake_images) 
+        outputs = discriminator(fake_images.detach()) 
         fake_loss = criterion(outputs, fake_labels)
         fake_score = outputs
         
@@ -131,4 +131,4 @@ for epoch in range(50):
 
 # Save the Models 
 torch.save(generator.state_dict(), './generator.pkl')
-torch.save(discriminator.state_dict(), './discriminator.pkl')
+torch.save(discriminator.state_dict(), './discriminator.pkl')

tutorials/11 - Deep Convolutional Generative Adversarial Network/main.py

@@ -102,7 +102,7 @@ for epoch in range(50):
         
         noise = Variable(torch.randn(images.size(0), 128))
         fake_images = generator(noise)
-        outputs = discriminator(fake_images) 
+        outputs = discriminator(fake_images.detch()) 
         fake_loss = criterion(outputs, fake_labels)
         fake_score = outputs
         
@@ -131,4 +131,4 @@ for epoch in range(50):
 
 # Save the Models
 torch.save(generator.state_dict(), './generator.pkl')
-torch.save(discriminator.state_dict(), './discriminator.pkl')
+torch.save(discriminator.state_dict(), './discriminator.pkl')