Natuurlijk! Hier is een overzicht van de essentiële stappen en enkele voorbeeldcodefragmenten voor het ontwikkelen van een PyTorch-model voor few-shot leren, specifiek voor je dataset met 10 rassen en 5 voorbeelden per ras. **1. Gegevensvoorbereiding** Omdat je slechts 5 voorbeelden per klasse hebt, moet je je dataset zorgvuldig voorbereiden: - **Data laden en organiseren:** Groepeer de data per klasse. - **Augmentaties:** Pas augmentaties toe om de variatie te vergroten (bijvoorbeeld rotatie, zoom, kleurveranderingen). - **Episode-generatie:** Voor few-shot leren worden episodische training en testing gehanteerd, waarbij elke episode een kleine support set en query set bevat. ```python import torch from torchvision import datasets, transforms from torch.utils.data import DataLoader, Dataset import random # Voorbeeld augmentaties transform = transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor() ]) # Custom Dataset voor episodisch leren class FewShotDataset(Dataset): def __init__(self, data, labels, n_way, k_shot, q_queries): self.data = data self.labels = labels self.n_way = n_way self.k_shot = k_shot self.q_queries = q_queries self.classes = list(set(labels)) self.class_to_indices = {c: [i for i, lbl in enumerate(labels) if lbl == c] for c in self.classes} def __len__(self): return 1000 # aantal episodes def __getitem__(self, idx): # Kies n_way klassen selected_classes = random.sample(self.classes, self.n_way) support_images = [] support_labels = [] query_images = [] query_labels = [] for i, c in enumerate(selected_classes): indices = self.class_to_indices[c] selected_indices = random.sample(indices, self.k_shot + self.q_queries) support_idx = selected_indices[:self.k_shot] query_idx = selected_indices[self.k_shot:] for si in support_idx: support_images.append(self.data[si]) support_labels.append(i) for qi in query_idx: query_images.append(self.data[qi]) query_labels.append(i) support_images = torch.stack([transform(img) for img in support_images]) support_labels = torch.tensor(support_labels) query_images = torch.stack([transform(img) for img in query_images]) query_labels = torch.tensor(query_labels) return support_images, support_labels, query_images, query_labels ``` **2. Selectie van modelarchitectuur** Voor few-shot learning zijn prototypische netwerken populair: - **Prototypische Netwerken:** Berekenen prototype per klasse uit support set en classificeren query's op basis van afstand. Voorbeeld van een eenvoudig CNN als encoder: ```python import torch.nn as nn import torch.nn.functional as F class Encoder(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(3, 64, 3) self.conv2 = nn.Conv2d(64, 64, 3) self.conv3 = nn.Conv2d(64, 64, 3) self.fc = nn.Linear(64*20*20, 128) # afhankelijk van input grootte def forward(self, x): x = F.relu(self.conv1(x)) x = F.relu(self.conv2(x)) x = F.relu(self.conv3(x)) x = x.view(x.size(0), -1) x = self.fc(x) return x ``` **3. Trainingsstrategie en loss functie** - **Prototypische Netwerken:** Gebruik de Euclidische afstand tussen query's en prototypes. - **Loss:** Cross-entropy over de afgebeelde afstandsgebaseerde classifcatie. ```python def compute_prototypes(support_embeddings, support_labels, n_way, k_shot): prototypes = [] for c in range(n_way): class_mask = support_labels == c class_embeddings = support_embeddings[class_mask] prototype = class_embeddings.mean(0) prototypes.append(prototype) return torch.stack(prototypes) # Trainingstappen binnen een episode encoder = Encoder() optimizer = torch.optim.Adam(encoder.parameters(), lr=0.001) for epoch in range(100): support_images, support_labels, query_images, query_labels = next(iter(dataloader)) support_embeddings = encoder(support_images) query_embeddings = encoder(query_images) prototypes = compute_prototypes(support_embeddings, support_labels, n_way=10, k_shot=5) # Berekenen afstanden dists = torch.cdist(query_embeddings, prototypes) # Classificatie logits = -dists loss = F.cross_entropy(logits, query_labels) optimizer.zero_grad() loss.backward() optimizer.step() ``` **4. Samenvatting** - **Gegevens:** Organiseer je data per klasse, gebruik episodische training met support en query sets. - **Model:** Gebruik een encoder + prototypische aanpak. - **Training:** Optimaliseer door afstand-gebaseerde classificatie per episode. - **Evaluatie:** Test op nieuwe episodes, vergelijk prototypes. **Tip:** Pas de parameters aan (n_way, k_shot, q_queries) op basis van jouw dataset en vereisten. Indien je meer details wilt of hulp bij specifieke onderdelen, help ik je graag verder!

Zeker, ik help je graag met een few-shot learning model in PyTorch. Hier is een complete handleiding: ## 1. Gegevensvoorbereiding ### Episode-gebaseerde DataLoader ```python import torch from torch.utils.data import DataLoader, Dataset import torchvision.transforms as transforms from PIL import Image import os class FewShotDataset(Dataset): def __init__(self, data_path, n_way=5, k_shot=1, n_query=5, transform=None): self.data_path = data_path self.n_way = n_way self.k_shot = k_shot self.n_query = n_query self.transform = transform self.classes = os.listdir(data_path) self.samples = self._load_samples() def _load_samples(self): samples = {} for class_name in self.classes: class_path = os.path.join(self.data_path, class_name) images = [os.path.join(class_path, img) for img in os.listdir(class_path)] samples[class_name] = images return samples def __getitem__(self, index): # Selecteer willekeurige klassen voor deze episode selected_classes = torch.randperm(len(self.classes))[:self.n_way] support_set = [] query_set = [] query_labels = [] for i, class_idx in enumerate(selected_classes): class_name = self.classes[class_idx] class_samples = self.samples[class_name] # Selecteer support samples support_indices = torch.randperm(len(class_samples))[:self.k_shot] for idx in support_indices: img = Image.open(class_samples[idx]) if self.transform: img = self.transform(img) support_set.append((img, i)) # Selecteer query samples query_indices = torch.randperm(len(class_samples))[self.k_shot:self.k_shot + self.n_query] for idx in query_indices: img = Image.open(class_samples[idx]) if self.transform: img = self.transform(img) query_set.append(img) query_labels.append(i) # Combineer support set support_images = torch.stack([x[0] for x in support_set]) support_labels = torch.tensor([x[1] for x in support_set]) query_images = torch.stack(query_set) query_labels = torch.tensor(query_labels) return support_images, support_labels, query_images, query_labels def __len__(self): return 1000 # Aantal episodes per epoch ``` ## 2. Modelarchitectuur - Prototypische Netwerken ```python import torch.nn as nn import torch.nn.functional as F class EmbeddingNet(nn.Module): def __init__(self, input_dim=3, hidden_dim=64, output_dim=64): super(EmbeddingNet, self).__init__() self.conv_net = nn.Sequential( nn.Conv2d(input_dim, hidden_dim, 3, padding=1), nn.BatchNorm2d(hidden_dim), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1), nn.BatchNorm2d(hidden_dim), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1), nn.BatchNorm2d(hidden_dim), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(hidden_dim, output_dim, 3, padding=1), nn.BatchNorm2d(output_dim), nn.ReLU(), nn.AdaptiveAvgPool2d(1) ) def forward(self, x): x = self.conv_net(x) return x.view(x.size(0), -1) class PrototypicalNetwork(nn.Module): def __init__(self, encoder): super(PrototypicalNetwork, self).__init__() self.encoder = encoder def forward(self, support_x, support_y, query_x): # Bereken embeddings support_emb = self.encoder(support_x) query_emb = self.encoder(query_x) # Bereken prototypen voor elke klasse prototypes = [] n_way = len(torch.unique(support_y)) for i in range(n_way): mask = support_y == i class_emb = support_emb[mask] prototype = class_emb.mean(dim=0) prototypes.append(prototype) prototypes = torch.stack(prototypes) # Bereken Euclidean afstanden dists = self.euclidean_dist(query_emb, prototypes) # Converteer naar waarschijnlijkheden logits = -dists return logits def euclidean_dist(self, x, y): n = x.size(0) m = y.size(0) d = x.size(1) x = x.unsqueeze(1).expand(n, m, d) y = y.unsqueeze(0).expand(n, m, d) return torch.pow(x - y, 2).sum(2) ``` ## 3. Trainingsstrategie ```python def train_prototypical_network(model, train_loader, val_loader, epochs=100): optimizer = torch.optim.Adam(model.parameters(), lr=0.001) scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5) for epoch in range(epochs): model.train() total_loss = 0 total_acc = 0 for batch_idx, (support_x, support_y, query_x, query_y) in enumerate(train_loader): optimizer.zero_grad() logits = model(support_x, support_y, query_x) loss = F.cross_entropy(logits, query_y) loss.backward() optimizer.step() # Bereken accuracy pred = logits.argmax(dim=1) acc = (pred == query_y).float().mean() total_loss += loss.item() total_acc += acc.item() scheduler.step() # Validering val_acc = evaluate(model, val_loader) print(f'Epoch {epoch+1}/{epochs}, Loss: {total_loss/len(train_loader):.4f}, ' f'Train Acc: {total_acc/len(train_loader):.4f}, Val Acc: {val_acc:.4f}') def evaluate(model, data_loader): model.eval() total_acc = 0 with torch.no_grad(): for support_x, support_y, query_x, query_y in data_loader: logits = model(support_x, support_y, query_x) pred = logits.argmax(dim=1) acc = (pred == query_y).float().mean() total_acc += acc.item() return total_acc / len(data_loader) ``` ## 4. Complete Trainingspijplijn ```python # Data transformaties transform = transforms.Compose([ transforms.Resize((84, 84)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) # Maak datasets en dataloaders train_dataset = FewShotDataset( data_path='pad/naar/train_data', n_way=5, # 5 klassen per episode k_shot=1, # 1 voorbeeld per klasse voor support n_query=5, # 5 voorbeelden per klasse voor query transform=transform ) val_dataset = FewShotDataset( data_path='pad/naar/val_data', n_way=5, k_shot=1, n_query=5, transform=transform ) train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False) # Initialiseer model encoder = EmbeddingNet(input_dim=3, hidden_dim=64, output_dim=64) model = PrototypicalNetwork(encoder) # Train het model train_prototypical_network(model, train_loader, val_loader, epochs=100) ``` ## 5. Alternatieve Architectuur: Matching Networks ```python class MatchingNetwork(nn.Module): def __init__(self, encoder): super(MatchingNetwork, self).__init__() self.encoder = encoder self.attention = nn.MultiheadAttention(embed_dim=64, num_heads=8) def forward(self, support_x, support_y, query_x): support_emb = self.encoder(support_x) query_emb = self.encoder(query_x) # Gebruik attention voor matching attn_output, _ = self.attention( query_emb.unsqueeze(1), support_emb.unsqueeze(1), support_emb.unsqueeze(1) ) # Bereken similarity scores similarities = F.cosine_similarity( query_emb.unsqueeze(1), support_emb.unsqueeze(0), dim=2 ) # Aggregate over support set n_way = len(torch.unique(support_y)) logits = [] for i in range(n_way): mask = (support_y == i).float() class_similarity = (similarities * mask).sum(dim=1) logits.append(class_similarity) logits = torch.stack(logits, dim=1) return logits ``` ## Belangrijke Tips voor Few-Shot Learning: 1. **Data Augmentation**: Gebruik uitgebreide augmentatie (rotaties, flipping, color jitter) 2. **Episode Training**: Train met willekeurige N-way K-shot episodes 3. **Learning Rate Scheduling**: Gebruik cosine annealing of step decay 4. **Embedding Dimensie**: Experimenteer met verschillende embedding groottes (64, 128, 256) 5. **Transfer Learning**: Pre-train de encoder op een grotere dataset indien mogelijk Deze implementatie is specifiek ontworpen voor jouw dataset met 10 rassen en 5 voorbeelden per ras. Pas de parameters aan op basis van je specifieke behoeften en datasetkenmerken.