File size: 2,019 Bytes
2a84868 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder
import os
# Set device (GPU if available, otherwise CPU)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Define data transforms
data_transforms = transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# Load dataset
dataset = ImageFolder(root="./synthetic_dataset", transform=data_transforms)
# Create data loader
batch_size = 32
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
# Define CNN model
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(6 * 14 * 14, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 3)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = x.view(-1, 6 * 14 * 14)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
model = CNN().to(device)
# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-3)
# Train model
for epoch in range(5):
running_loss = 0.0
for i, data in enumerate(data_loader):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
avg_loss = running_loss / (i + 1)
print(f"Epoch {epoch+1}/5 — Loss: {avg_loss:.4f}")
# Save model weights
torch.save(model.state_dict(), "best_model.pt")
print("Training complete. Model saved to best_model.pt") |