DataLoader kullanma

DataLoader sınıfı, büyük veri kümelerini verimli bir şekilde işlemek için gereklidir. Eğitimi hızlandırır, bellek kullanımını optimize eder ve gradyan güncellemelerini dengeler; böylece derin öğrenme modellerini daha etkili hale getirir.

Şimdi, önceki egzersizdeki dataset ile bir PyTorch DataLoader oluşturacak ve nasıl çalıştığını göreceksin.

Bu egzersiz

PyTorch ile Deep Learning'e Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Gerekli modülü içe aktar.
dataset kullanarak bir DataLoader oluştur; yığın boyutunu iki olarak ayarla ve karıştırmayı etkinleştir.
DataLoader üzerinde yineleme yap ve her bir girdi ve etiket yığınını yazdır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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)

Kodu Düzenle ve Çalıştır

Bu egzersiz

PyTorch ile Deep Learning'e Giriş

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Veri yüklemeye daha derin bir bakış Exercise 2: TensorDataset kullanma Exercise 3: DataLoader kullanma

Geçerli Egzersiz

Exercise 4: İlk eğitim döngümüzü yazmak Exercise 5: MSELoss kullanma Exercise 6: Bir eğitim döngüsü yazmak Exercise 7: ReLU aktivasyon fonksiyonları Exercise 8: ReLU'yu Uygulama Exercise 9: Leaky ReLU uygulama Exercise 10: Etkinleştirme fonksiyonlarını anlama Exercise 11: Öğrenme oranı ve momentum Exercise 12: Öğrenme oranıyla denemeler Exercise 13: Momentum ile denemeler

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