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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import sys
dataset="MNIST"
if(len(sys.argv) > 1):
if(sys.argv[1].upper() == "F" or sys.argv[1].upper() == "FASHION"):
dataset="FashionMNIST"
# Define the CNN architecture
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)
self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=0)
self.fc1 = nn.Linear(in_features=400, out_features=120)
self.fc2 = nn.Linear(in_features=120, out_features=84)
self.fc3 = nn.Linear(in_features=84, out_features=10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, kernel_size=2, stride=2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, kernel_size=2, stride=2)
x = torch.flatten(x, 1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
# Hyperparameters
batch_size = 64
test_batch_size = 1000
epochs = 10
learning_rate = 0.001
log_interval = 100 # Log training progress every 100 batches
# Set device (GPU if available, else CPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
# Data preprocessing and augmentation
transform = transforms.Compose([
transforms.ToTensor(), # Convert PIL images to tensors
transforms.Normalize((0.1307,), (0.3081,)) # Normalize images
])
# Load datasets
if(dataset=="FashionMNIST"):
train_dataset = datasets.FashionMNIST('data', train=True, download=True, transform=transform)
test_dataset = datasets.FashionMNIST('data', train=False, transform=transform)
else:
train_dataset = datasets.MNIST('data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST('data', train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=test_batch_size, shuffle=False)
# Initialize model and optimizer
model = Net().to(device)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss() # Combines softmax and NLL loss
# Training function
def train(model, device, train_loader, optimizer, epoch):
model.train()
losses = []
correct = 0
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
losses.append(loss.item())
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
if batch_idx % log_interval == 0 and batch_idx != 0:
print(f'{dataset}: Epoch {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)}] Loss: {loss.item():.6f}')
avg_loss = sum(losses) / len(losses)
accuracy = 100. * correct / len(train_loader.dataset)
print(f'{dataset}: Epoch {epoch} - Avg Loss: {avg_loss:.6f}, Accuracy: {accuracy:.2f}%')
# Test function
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += criterion(output, target).item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / len(test_loader.dataset)
print(f'\nTest Set - Loss: {test_loss:.4f}, Accuracy: {accuracy:.2f}%\n')
# Training loop
for epoch in range(1, epochs + 1):
train(model, device, train_loader, optimizer, epoch)
test(model, device, test_loader)
# Save the trained model
torch.save(model.state_dict(), "mnist_cnn.pth")
print("Model saved as mnist_cnn.pth")
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