tutorials are added

tutorials/04 - Convolutional Neural Network/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
+num_epochs = 5
+batch_size = 100
+learning_rate = 0.001
+
+# MNIST Dataset
+train_dataset = dsets.MNIST(root='./data/',
+                            train=True, 
+                            transform=transforms.ToTensor(),
+                            download=True)
+
+test_dataset = dsets.MNIST(root='./data/',
+                           train=False, 
+                           transform=transforms.ToTensor())
+
+# Data Loader (Input Pipeline)
+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)
+
+# CNN Model (2 conv layer)
+class CNN(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.layer1 = nn.Sequential(
+            nn.Conv2d(1, 16, kernel_size=5, padding=2),
+            nn.BatchNorm2d(16),
+            nn.ReLU(),
+            nn.MaxPool2d(2))
+        self.layer2 = nn.Sequential(
+            nn.Conv2d(16, 32, kernel_size=5, padding=2),
+            nn.BatchNorm2d(32),
+            nn.ReLU(),
+            nn.MaxPool2d(2))
+        self.fc = nn.Linear(7*7*32, 10)
+        
+    def forward(self, x):
+        out = self.layer1(x)
+        out = self.layer2(out)
+        out = out.view(out.size(0), -1)
+        out = self.fc(out)
+        return out
+        
+cnn = CNN()
+
+
+# Loss and Optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)
+
+# Train the Model
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):
+        images = Variable(images)
+        labels = Variable(labels)
+        
+        # Forward + Backward + Optimize
+        optimizer.zero_grad()
+        outputs = cnn(images)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+        
+        if (i+1) % 100 == 0:
+            print ('Epoch [%d/%d], Iter [%d/%d] Loss: %.4f' 
+                   %(epoch+1, num_epochs, i+1, len(train_dataset)//batch_size, loss.data[0]))
+
+# Test the Model
+cnn.eval()  
+correct = 0
+total = 0
+for images, labels in test_loader:
+    images = Variable(images)
+    outputs = cnn(images)
+    _, predicted = torch.max(outputs.data, 1)
+    total += labels.size(0)
+    correct += (predicted == labels).sum()
+
+print('Test Accuracy of the model on the 10000 test images: %d %%' % (100 * correct / total))