Menyempurnakan metode forward

Setelah menyiapkan layer di metode __init__, metode forward menentukan bagaimana data mengalir melaluinya. Di PyTorch Lightning, pemisahan ini membuat kode Anda tetap rapi dan mudah dipelihara. Anda telah melihat cara menyusun konstruktor—sekarang saatnya berfokus pada forward pass, memastikan logika klasifikasi Anda jelas dan dioptimalkan untuk pelatihan. Di sini, layer pada __init__ sudah didefinisikan untuk Anda, sehingga Anda dapat berkonsentrasi murni pada alur forward.

lightning.pytorch dan torch.nn telah diimpor sebagai pl dan nn.

Latihan ini adalah bagian dari kursus

Model AI yang Dapat Diskalakan dengan PyTorch Lightning

Petunjuk latihan

Implementasikan metode forward di dalam ClassifierModel.
Terapkan aktivasi ReLU setelah hidden layer.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

class ClassifierModel(pl.LightningModule):
  
    def __init__(self, input_dim, hidden_dim, output_dim):
        super().__init__()
        self.hidden = nn.Linear(input_dim, hidden_dim)
        self.output = nn.Linear(hidden_dim, output_dim)
        
    # Define forward method
    def ____(self, ____):
        # Complete the forward pass
        x = self.hidden(x)
        x = ____(x)
        x = self.output(x)
        return x

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Model AI yang Dapat Diskalakan dengan PyTorch Lightning

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, we'll explore how PyTorch Lightning simplifies the development and deployment of scalable AI models. Starting with foundational concepts, we'll go through the core structure of a PyTorch Lightning project, including essential components like the LightningModule and Trainer, to set a strong foundation for more advanced AI solutions.

Exercise 1: Pengantar PyTorch Lightning Exercise 2: Mengenal LightningModule Exercise 3: Menjalankan Lightning Trainer Exercise 4: Mendefinisikan model dengan LightningModule Exercise 5: Penggunaan LightningModule Exercise 6: Menguasai metode init Exercise 7: Menyempurnakan metode forward

Latihan Saat Ini

Exercise 8: Menerapkan logika pelatihan Exercise 9: Mengimplementasikan langkah pelatihan Exercise 10: Mengonfigurasi pengoptimal Exercise 11: Melatih dan mengevaluasi

We'll dive deeper into PyTorch Lightning to efficiently manage data and refine model training in this chapter. We'll learn how to create modular and reusable data workflows with LightningDataModule, evaluate your models accurately through validation and testing, and enhance training processes using Lightning Callbacks to automate model improvement and avoid overfitting.

Exercise 1: Managing data with LightningDataModule Exercise 2: Splitting data with LightningDataModule Exercise 3: Creating a train DataLoader Exercise 4: Incorporating validation and testing Exercise 5: Implementing the validation step Exercise 6: Evaluate model accuracy using Torchmetrics Exercise 7: Enhancing training with Lightning callbacks Exercise 8: Classifying Lightning callbacks Exercise 9: Optimizing model training with Lightning

Learn to prepare deep learning models for real-world deployment by making them leaner and faster. This chapter introduces techniques such as dynamic quantization, pruning, and TorchScript conversion, helping you reduce model size and latency without sacrificing accuracy

Exercise 1: Applying dynamic quantization Exercise 2: Apply dynamic quantization Exercise 3: Comparing quantized model performance Exercise 4: Implementing model pruning techniques Exercise 5: Apply pruning to a linear layer Exercise 6: Finalize pruning by removing the mask Exercise 7: Exporting models with TorchScript Exercise 8: Choosing the right conversion method Exercise 9: Optimizing models for scalability Exercise 10: Recap: Scalable AI Models with PyTorch Lightning