Merge pull request #120 from arisliang/master

minor refactoring and fix

tutorials/01-basics/logistic_regression/main.py

@@ -5,7 +5,7 @@ import torchvision.transforms as transforms
 
 
 # Hyper-parameters 
-input_size = 784
+input_size = 28 * 28    # 784
 num_classes = 10
 num_epochs = 5
 batch_size = 100
@@ -43,7 +43,7 @@ total_step = len(train_loader)
 for epoch in range(num_epochs):
     for i, (images, labels) in enumerate(train_loader):
         # Reshape images to (batch_size, input_size)
-        images = images.reshape(-1, 28*28)
+        images = images.reshape(-1, input_size)
         
         # Forward pass
         outputs = model(images)
@@ -64,7 +64,7 @@ with torch.no_grad():
     correct = 0
     total = 0
     for images, labels in test_loader:
-        images = images.reshape(-1, 28*28)
+        images = images.reshape(-1, input_size)
         outputs = model(images)
         _, predicted = torch.max(outputs.data, 1)
         total += labels.size(0)

tutorials/02-intermediate/deep_residual_network/main.py

@@ -16,6 +16,7 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 # Hyper-parameters
 num_epochs = 80
+batch_size = 100
 learning_rate = 0.001
 
 # Image preprocessing modules
@@ -37,11 +38,11 @@ test_dataset = torchvision.datasets.CIFAR10(root='../../data/',
 
 # Data loader
 train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
-                                           batch_size=100, 
+                                           batch_size=batch_size,
                                            shuffle=True)
 
 test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
-                                          batch_size=100, 
+                                          batch_size=batch_size,
                                           shuffle=False)
 
 # 3x3 convolution

tutorials/02-intermediate/recurrent_neural_network/main.py

@@ -85,6 +85,7 @@ for epoch in range(num_epochs):
                    .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
 
 # Test the model
+model.eval()
 with torch.no_grad():
     correct = 0
     total = 0