On this tutorial, we uncover the power of self-supervised learning using the Flippantly AI framework. We begin by developing a SimCLR model to be taught vital image representations with out labels, then generate and visualize embeddings using UMAP and t-SNE. We then dive into coreset selection strategies to curate information intelligently, simulate an vigorous learning workflow, and finally assess the benefits of swap learning via a linear probe evaluation. All via this hands-on data, we work step-by-step in Google Colab, teaching, visualizing, and evaluating coreset-based and random sampling to know the way self-supervised learning can significantly improve information effectivity and model effectivity. Strive the FULL CODES proper right here.
!pip uninstall -y numpy
!pip arrange numpy==1.26.4
!pip arrange -q flippantly torch torchvision matplotlib scikit-learn umap-learn
import torch
import torch.nn as nn
import torchvision
from torch.utils.information import DataLoader, Subset
from torchvision import transforms
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.neighbors import NearestNeighbors
import umap
from flippantly.loss import NTXentLoss
from flippantly.fashions.modules import SimCLRProjectionHead
from flippantly.transforms import SimCLRTransform
from flippantly.information import LightlyDataset
print(f"PyTorch mannequin: {torch.__version__}")
print(f"CUDA accessible: {torch.cuda.is_available()}")We begin by establishing the setting, guaranteeing compatibility by fixing the NumPy mannequin and placing in necessary libraries like Flippantly, PyTorch, and UMAP. We then import all important modules for developing, teaching, and visualizing our self-supervised learning model, confirming that PyTorch and CUDA are ready for GPU acceleration. Strive the FULL CODES proper right here.
class SimCLRModel(nn.Module):
"""SimCLR model with ResNet backbone"""
def __init__(self, backbone, hidden_dim=512, out_dim=128):
great().__init__()
self.backbone = backbone
self.backbone.fc = nn.Identification()
self.projection_head = SimCLRProjectionHead(
input_dim=512, hidden_dim=hidden_dim, output_dim=out_dim
)
def forward(self, x):
choices = self.backbone(x).flatten(start_dim=1)
z = self.projection_head(choices)
return z
def extract_features(self, x):
"""Extract backbone choices with out projection"""
with torch.no_grad():
return self.backbone(x).flatten(start_dim=1)We define our SimCLRModel, which makes use of a ResNet backbone to be taught seen representations with out labels. We take away the classification head and add a projection head to map choices proper right into a contrastive embedding space. The model’s extract_features methodology permits us to amass raw operate embeddings instantly from the backbone for downstream analysis. Strive the FULL CODES proper right here.
def load_dataset(put together=True):
"""Load CIFAR-10 dataset"""
ssl_transform = SimCLRTransform(input_size=32, cj_prob=0.8)
eval_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])
base_dataset = torchvision.datasets.CIFAR10(
root="./information", put together=put together, get hold of=True
)
class SSLDataset(torch.utils.information.Dataset):
def __init__(self, dataset, rework):
self.dataset = dataset
self.rework = rework
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
img, label = self.dataset[idx]
return self.rework(img), label
ssl_dataset = SSLDataset(base_dataset, ssl_transform)
eval_dataset = torchvision.datasets.CIFAR10(
root="./information", put together=put together, get hold of=True, rework=eval_transform
)
return ssl_dataset, eval_datasetOn this step, we load the CIFAR-10 dataset and apply separate transformations for self-supervised and evaluation phases. We create a personalized SSLDataset class that generates various augmented views of each image for contrastive learning, whereas the evaluation dataset makes use of normalized images for downstream duties. This setup helps the model be taught robust representations invariant to seen changes. Strive the FULL CODES proper right here.
def train_ssl_model(model, dataloader, epochs=5, machine="cuda"):
"""Observe SimCLR model"""
model.to(machine)
criterion = NTXentLoss(temperature=0.5)
optimizer = torch.optim.SGD(model.parameters(), lr=0.06, momentum=0.9, weight_decay=5e-4)
print("n=== Self-Supervised Teaching ===")
for epoch in range(epochs):
model.put together()
total_loss = 0
for batch_idx, batch in enumerate(dataloader):
views = batch[0]
view1, view2 = views[0].to(machine), views[1].to(machine)
z1 = model(view1)
z2 = model(view2)
loss = criterion(z1, z2)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.merchandise()
if batch_idx % 50 == 0:
print(f"Epoch {epoch+1}/{epochs} | Batch {batch_idx} | Loss: {loss.merchandise():.4f}")
avg_loss = total_loss / len(dataloader)
print(f"Epoch {epoch+1} Full | Avg Loss: {avg_loss:.4f}")
return modelProper right here, we put together our SimCLR model in a self-supervised methodology using the NT-Xent contrastive loss, which conjures up associated representations for augmented views of the equivalent image. We optimize the model with stochastic gradient descent (SGD) and monitor the loss all through epochs to look at learning progress. This stage teaches the model to extract vital seen choices with out relying on labeled information. Strive the FULL CODES proper right here.
def generate_embeddings(model, dataset, machine="cuda", batch_size=256):
"""Generate embeddings for the entire dataset"""
model.eval()
model.to(machine)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=2)
embeddings = []
labels = []
print("n=== Producing Embeddings ===")
with torch.no_grad():
for images, targets in dataloader:
images = images.to(machine)
choices = model.extract_features(images)
embeddings.append(choices.cpu().numpy())
labels.append(targets.numpy())
embeddings = np.vstack(embeddings)
labels = np.concatenate(labels)
print(f"Generated {embeddings.kind[0]} embeddings with dimension {embeddings.kind[1]}")
return embeddings, labels
def visualize_embeddings(embeddings, labels, methodology='umap', n_samples=5000):
"""Visualize embeddings using UMAP or t-SNE"""
print(f"n=== Visualizing Embeddings with {methodology.greater()} ===")
if len(embeddings) > n_samples:
indices = np.random.different(len(embeddings), n_samples, change=False)
embeddings = embeddings[indices]
labels = labels[indices]
if methodology == 'umap':
reducer = umap.UMAP(n_neighbors=15, min_dist=0.1, metric="cosine")
else:
reducer = TSNE(n_components=2, perplexity=30, metric="cosine")
embeddings_2d = reducer.fit_transform(embeddings)
plt.decide(figsize=(12, 10))
scatter = plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1],
c=labels, cmap='tab10', s=5, alpha=0.6)
plt.colorbar(scatter)
plt.title(f'CIFAR-10 Embeddings ({methodology.greater()})')
plt.xlabel('Half 1')
plt.ylabel('Half 2')
plt.tight_layout()
plt.savefig(f'embeddings_{methodology}.png', dpi=150)
print(f"Saved visualization to embeddings_{methodology}.png")
plt.current()
def select_coreset(embeddings, labels, funds=1000, methodology='vary'):
"""
Select a coreset using completely totally different strategies:
- vary: Most vary using k-center greedy
- balanced: Class-balanced selection
"""
print(f"n=== Coreset Selection ({methodology}) ===")
if methodology == 'balanced':
selected_indices = []
n_classes = len(np.distinctive(labels))
per_class = funds // n_classes
for cls in range(n_classes):
cls_indices = np.the place(labels == cls)[0]
chosen = np.random.different(cls_indices, min(per_class, len(cls_indices)), change=False)
selected_indices.extend(chosen)
return np.array(selected_indices)
elif methodology == 'vary':
selected_indices = []
remaining_indices = set(range(len(embeddings)))
first_idx = np.random.randint(len(embeddings))
selected_indices.append(first_idx)
remaining_indices.take away(first_idx)
for _ in range(funds - 1):
if not remaining_indices:
break
remaining = itemizing(remaining_indices)
selected_emb = embeddings[selected_indices]
remaining_emb = embeddings[remaining]
distances = np.min(
np.linalg.norm(remaining_emb[:, None] - selected_emb, axis=2), axis=1
)
max_dist_idx = np.argmax(distances)
selected_idx = remaining[max_dist_idx]
selected_indices.append(selected_idx)
remaining_indices.take away(selected_idx)
print(f"Chosen {len(selected_indices)} samples")
return np.array(selected_indices)We extract high-quality operate embeddings from our expert backbone, cache them with labels, and problem them to 2D using UMAP or t-SNE to visually see the cluster development emerge. Subsequent, we curate information using a coreset selector, each class-balanced or diversity-driven (k-center greedy), to prioritize basically essentially the most informative, non-redundant samples for downstream teaching. This pipeline helps us every see what the model learns and select what points most. Strive the FULL CODES proper right here.
def evaluate_linear_probe(model, train_subset, test_dataset, machine="cuda"):
"""Observe linear classifier on frozen choices"""
model.eval()
train_loader = DataLoader(train_subset, batch_size=128, shuffle=True, num_workers=2)
test_loader = DataLoader(test_dataset, batch_size=256, shuffle=False, num_workers=2)
classifier = nn.Linear(512, 10).to(machine)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(classifier.parameters(), lr=0.001)
for epoch in range(10):
classifier.put together()
for images, targets in train_loader:
images, targets = images.to(machine), targets.to(machine)
with torch.no_grad():
choices = model.extract_features(images)
outputs = classifier(choices)
loss = criterion(outputs, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
classifier.eval()
applicable = 0
full = 0
with torch.no_grad():
for images, targets in test_loader:
images, targets = images.to(machine), targets.to(machine)
choices = model.extract_features(images)
outputs = classifier(choices)
_, predicted = outputs.max(1)
full += targets.measurement(0)
applicable += predicted.eq(targets).sum().merchandise()
accuracy = 100. * applicable / full
return accuracy
def elementary():
machine="cuda" if torch.cuda.is_available() else 'cpu'
print(f"Using machine: {machine}")
ssl_dataset, eval_dataset = load_dataset(put together=True)
_, test_dataset = load_dataset(put together=False)
ssl_subset = Subset(ssl_dataset, range(10000))
ssl_loader = DataLoader(ssl_subset, batch_size=128, shuffle=True, num_workers=2, drop_last=True)
backbone = torchvision.fashions.resnet18(pretrained=False)
model = SimCLRModel(backbone)
model = train_ssl_model(model, ssl_loader, epochs=5, machine=machine)
eval_subset = Subset(eval_dataset, range(10000))
embeddings, labels = generate_embeddings(model, eval_subset, machine=machine)
visualize_embeddings(embeddings, labels, methodology='umap')
coreset_indices = select_coreset(embeddings, labels, funds=1000, methodology='vary')
coreset_subset = Subset(eval_dataset, coreset_indices)
print("n=== Energetic Finding out Evaluation ===")
coreset_acc = evaluate_linear_probe(model, coreset_subset, test_dataset, machine=machine)
print(f"Coreset Accuracy (1000 samples): {coreset_acc:.2f}%")
random_indices = np.random.different(len(eval_subset), 1000, change=False)
random_subset = Subset(eval_dataset, random_indices)
random_acc = evaluate_linear_probe(model, random_subset, test_dataset, machine=machine)
print(f"Random Accuracy (1000 samples): {random_acc:.2f}%")
print(f"nCoreset enchancment: +{coreset_acc - random_acc:.2f}%")
print("n=== Tutorial Full! ===")
print("Key takeaways:")
print("1. Self-supervised learning creates vital representations with out labels")
print("2. Embeddings seize semantic similarity between images")
print("3. Good information selection (coreset) outperforms random sampling")
print("4. Energetic learning reduces labeling costs whereas sustaining accuracy")
if __name__ == "__main__":
elementary()We freeze the backbone and put together a lightweight linear probe to quantify how good our realized choices are, then think about accuracy on the check out set. Within the major pipeline, we pretrain with SimCLR, generate embeddings, visualize them, select a numerous coreset, and look at linear-probe effectivity in the direction of a random subset, thereby instantly measuring the value of excellent information curation.
In conclusion, we’ve seen how self-supervised learning permits illustration learning with out information annotations and the way in which coreset-based information selection enhances model generalization with fewer samples. By teaching a SimCLR model, producing embeddings, curating information, and evaluating via vigorous learning, we experience the end-to-end course of of current self-supervised workflows. We conclude that by combining intelligent information curation with realized representations, we’re capable of assemble fashions which could be every resource-efficient and performance-optimized, setting a strong foundation for scalable machine learning functions.
Strive the FULL CODES proper right here. Be glad to try our GitHub Net web page for Tutorials, Codes and Notebooks. Moreover, be glad to watch us on Twitter and don’t overlook to affix our 100k+ ML SubReddit and Subscribe to our Publication. Wait! are you on telegram? now it’s possible you’ll be a part of us on telegram as properly.
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is devoted to harnessing the potential of Artificial Intelligence for social good. His latest endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth safety of machine learning and deep learning data that’s every technically sound and easily understandable by a big viewers. The platform boasts of over 2 million month-to-month views, illustrating its recognition amongst audiences.
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