tutorials are added

tutorials/02 - Logistic Regression/main.py

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+import torch
+import torch.nn as nn
+import torchvision.datasets as dsets
+import torchvision.transforms as transforms
+from torch.autograd import Variable
+
+
+# Hyper Parameters 
+input_size = 784
+num_classes = 10
+num_epochs = 5
+batch_size = 100
+learning_rate = 0.001
+
+# MNIST Dataset (Images and Labels)
+train_dataset = dsets.MNIST(root='./data', 
+                            train=True, 
+                            transform=transforms.ToTensor(),
+                            download=True)
+
+test_dataset = dsets.MNIST(root='./data', 
+                           train=False, 
+                           transform=transforms.ToTensor())
+
+# Dataset Loader (Input Pipline)
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset, 
+                                           batch_size=batch_size, 
+                                           shuffle=True)
+
+test_loader = torch.utils.data.DataLoader(dataset=test_dataset, 
+                                          batch_size=batch_size, 
+                                          shuffle=False)
+
+# Model
+class LogisticRegression(nn.Module):
+    def __init__(self, input_size, num_classes):
+        super(LogisticRegression, self).__init__()
+        self.linear = nn.Linear(input_size, num_classes)
+    
+    def forward(self, x):
+        out = self.linear(x)
+        return out
+
+model = LogisticRegression(input_size, num_classes)
+
+# Loss and Optimizer
+# Softmax is internally computed.
+# Set parameters to be updated.
+criterion = nn.CrossEntropyLoss()  
+optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  
+
+# Training the Model
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):
+        images = Variable(images.view(-1, 28*28))
+        labels = Variable(labels)
+        
+        # Forward + Backward + Optimize
+        optimizer.zero_grad()
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+        
+        if (i+1) % 100 == 0:
+            print ('Epoch: [%d/%d], Step: [%d/%d], Loss: %.4f' 
+                   % (epoch+1, num_epochs, i+1, len(train_dataset)//batch_size, loss.data[0]))
+
+# Test the Model
+correct = 0
+total = 0
+for images, labels in test_loader:
+    images = Variable(images.view(-1, 28*28))
+    outputs = model(images)
+    _, predicted = torch.max(outputs.data, 1)
+    total += labels.size(0)
+    correct += (predicted == labels).sum()
+    
+print('Accuracy of the model on the 10000 test images: %d %%' % (100 * correct / total))