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| import torch | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| from torch.utils.data import DataLoader, TensorDataset | |
| from torchvision.transforms import Compose, ToTensor, Normalize | |
| from sklearn.metrics import accuracy_score | |
| import h5py | |
| # Veriyi yüklemek için bir fonksiyon | |
| def load_hdf5_dataset(path): | |
| with h5py.File(path, "r") as f: | |
| images = torch.tensor(f['images'][:]) # Görseller | |
| labels = torch.tensor(f['labels'][:]) # Etiketler | |
| return images, labels | |
| # Verileri yükleme | |
| train_images, train_labels = load_hdf5_dataset('train_dataset.hdf5') | |
| test_images, test_labels = load_hdf5_dataset('test_dataset.hdf5') | |
| # Normalize etmek ve tensöre dönüştürmek için dönüşümler | |
| transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))]) | |
| # Verileri normalize et ve TensorDataset oluştur | |
| train_images = train_images.permute(0, 3, 1, 2) / 255.0 # Görüntüleri 0-1 aralığına getir | |
| test_images = test_images.permute(0, 3, 1, 2) / 255.0 | |
| train_dataset = TensorDataset(train_images, train_labels) | |
| test_dataset = TensorDataset(test_images, test_labels) | |
| # CNN Modelini Tanımlama | |
| class GalaxyCNN(nn.Module): | |
| def __init__(self): | |
| super(GalaxyCNN, self).__init__() | |
| self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1) | |
| self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1) | |
| self.pool = nn.MaxPool2d(2, 2) | |
| self.fc1 = nn.Linear(64 * 16 * 16, 128) # Fully connected layer | |
| self.fc2 = nn.Linear(128, 4) # 4 sınıf (smooth_round, smooth_cigar, vb.) | |
| self.relu = nn.ReLU() | |
| self.dropout = nn.Dropout(0.25) | |
| def forward(self, x): | |
| x = self.pool(self.relu(self.conv1(x))) | |
| x = self.pool(self.relu(self.conv2(x))) | |
| x = x.view(-1, 64 * 16 * 16) # Flatten | |
| x = self.dropout(self.relu(self.fc1(x))) | |
| x = self.fc2(x) | |
| return x | |
| # DataLoader | |
| batch_size = 32 | |
| train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) | |
| test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) | |
| # Model ve cihaz | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| model = GalaxyCNN().to(device) | |
| # Kayıp fonksiyonu ve optimizasyon | |
| criterion = nn.CrossEntropyLoss() | |
| optimizer = optim.Adam(model.parameters(), lr=0.001) | |
| # Eğitim | |
| epochs = 10 | |
| for epoch in range(epochs): | |
| model.train() | |
| running_loss = 0.0 | |
| for images, labels in train_loader: | |
| images, labels = images.to(device), labels.to(device) | |
| optimizer.zero_grad() | |
| outputs = model(images) | |
| loss = criterion(outputs, labels) | |
| loss.backward() | |
| optimizer.step() | |
| running_loss += loss.item() | |
| avg_loss = running_loss / len(train_loader) | |
| print(f"Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}") | |
| # Test ve doğrulama | |
| model.eval() | |
| all_preds = [] | |
| all_labels = [] | |
| with torch.no_grad(): | |
| for images, labels in test_loader: | |
| images, labels = images.to(device), labels.to(device) | |
| outputs = model(images) | |
| _, preds = torch.max(outputs, 1) | |
| all_preds.extend(preds.cpu().numpy()) | |
| all_labels.extend(labels.cpu().numpy()) | |
| accuracy = accuracy_score(all_labels, all_preds) | |
| print(f"Validation Accuracy: {accuracy * 100:.2f}%") | |
| torch.save(model.state_dict(), "galaxy_cnn_model.pt") | |
| print("Model kaydedildi: galaxy_cnn_model.pt") | |