Create main.py

Author: GAOYANGAU

convolutional_neural_network/main.py

@@ -0,0 +1,100 @@
+import torch 
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+
+# Device configuration
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
+# Hyper parameters
+num_epochs = 5
+num_classes = 10
+batch_size = 100
+learning_rate = 0.001
+
+# MNIST dataset
+train_dataset = torchvision.datasets.MNIST(root='../../data/',
+                                           train=True, 
+                                           transform=transforms.ToTensor(),
+                                           download=True)
+
+test_dataset = torchvision.datasets.MNIST(root='../../data/',
+                                          train=False, 
+                                          transform=transforms.ToTensor())
+
+# Data loader
+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)
+
+# Convolutional neural network (two convolutional layers)
+class ConvNet(nn.Module):
+    def __init__(self, num_classes=10):
+        super(ConvNet, self).__init__()
+        self.layer1 = nn.Sequential(
+            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
+            nn.BatchNorm2d(16),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2))
+        self.layer2 = nn.Sequential(
+            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
+            nn.BatchNorm2d(32),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2))
+        self.fc = nn.Linear(7*7*32, num_classes)
+        
+    def forward(self, x):
+        out = self.layer1(x)
+        out = self.layer2(out)
+        out = out.reshape(out.size(0), -1)
+        out = self.fc(out)
+        return out
+
+model = ConvNet(num_classes).to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+# Train the model
+total_step = len(train_loader)
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):
+        images = images.to(device)
+        labels = labels.to(device)
+        
+        # Forward pass
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+        if (i+1) % 100 == 0:
+            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
+                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
+
+# Test the model
+model.eval()  # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.to(device)
+        labels = labels.to(device)
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')