DataLoader gebruiken

De klasse DataLoader is essentieel voor het efficiënt verwerken van grote gegevenssets. Het versnelt het trainen, optimaliseert het geheugengebruik en stabiliseert de gradiëntupdates, waardoor deep learning-modellen effectiever worden.

Nu ga je een PyTorch DataLoader maken met de dataset uit de vorige oefening en zie je ‘m in actie.

Deze oefening maakt deel uit van de cursus

Introductie tot Deep Learning met PyTorch

Oefeninstructies

Importeer de benodigde module.
Maak een DataLoader met dataset, stel de batchgrootte in op twee en schakel schudden in.
Itereer door de DataLoader en print elke batch met inputs en labels.

Interactieve oefening met praktijkervaring

Probeer deze oefening door deze voorbeeldcode aan te vullen.

from torch.utils.data import ____

# Create a DataLoader
dataloader = ____

# Iterate over the dataloader
for batch_inputs, batch_labels in dataloader:
    print('batch_inputs:', batch_inputs)
    print('batch_labels:', batch_labels)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot Deep Learning met PyTorch

SkillTag.level.intermediateSkillTag.label

4.8+

Begin gratis met de cursus

Self-driving cars, smartphones, search engines... Deep learning is now everywhere. Before you begin building complex models, you will become familiar with PyTorch, a deep learning framework. You will learn how to manipulate tensors, create PyTorch data structures, and build your first neural network in PyTorch with linear layers.

Exercise 1: Introduction to deep learning with PyTorch Exercise 2: Getting started with PyTorch tensors Exercise 3: Checking and adding tensors Exercise 4: Neural networks and layers Exercise 5: Linear layer network Exercise 6: Understanding weights Exercise 7: Hidden layers and parameters Exercise 8: Your first neural network Exercise 9: Stacking linear layers Exercise 10: Counting the number of parameters

To train a neural network in PyTorch, you will first need to understand additional components, such as activation and loss functions. You will then realize that training a network requires minimizing that loss function, which is done by calculating gradients. You will learn how to use these gradients to update your model's parameters.

Exercise 1: Discovering activation functions Exercise 2: Activate your understanding!Exercise 3: The sigmoid and softmax functions Exercise 4: Running a forward pass Exercise 5: Building a binary classifier in PyTorch Exercise 6: From regression to multi-class classification Exercise 7: Using loss functions to assess model predictions Exercise 8: Creating one-hot encoded labels Exercise 9: Calculating cross entropy loss Exercise 10: Using derivatives to update model parameters Exercise 11: Accessing the model parameters Exercise 12: Updating the weights manually Exercise 13: Using the PyTorch optimizer

Now that you've learned the key components of a neural network, you'll train one using a training loop. You'll explore potential issues like vanishing gradients and learn strategies to address them, such as alternative activation functions and tuning learning rate and momentum.

Exercise 1: Dieper duiken in het laden van data Exercise 2: TensorDataset gebruiken Exercise 3: DataLoader gebruiken

Huidige oefening

Exercise 4: Onze eerste trainingslus schrijven Exercise 5: MSELoss gebruiken Exercise 6: Een trainingslus schrijven Exercise 7: ReLU-activatiefuncties Exercise 8: ReLU implementeren Exercise 9: Leaky ReLU implementeren Exercise 10: Activatiefuncties begrijpen Exercise 11: Learning rate en momentum Exercise 12: Experimenteren met de learning rate Exercise 13: Experimenteren met momentum

Training a deep learning model is an art, and to make sure our model is trained correctly, we need to keep track of certain metrics during training, such as the loss or the accuracy. We will learn how to calculate such metrics and how to reduce overfitting.

Exercise 1: Layer initialization and transfer learning Exercise 2: Fine-tuning process Exercise 3: Freeze layers of a model Exercise 4: Layer initialization Exercise 5: Evaluating model performance Exercise 6: Writing the evaluation loop Exercise 7: Calculating accuracy using torchmetrics Exercise 8: Fighting overfitting Exercise 9: Experimenting with dropout Exercise 10: Understanding overfitting Exercise 11: Improving model performance Exercise 12: Implementing random search Exercise 13: Wrap-up video