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Introducing the LightningModule

Get ready to build your first LightningModule! In this hands-on exercise, you'll set up the core structure of a classification workflow. You'll define a linear layer, pass data through it in the forward method, and compute the loss in the training step. This clean structure gives you a solid foundation to start experimenting with your models.

The torch and lightning.pytorch, imported as pl, have been preloaded for you.

この演習はコースの一部です

Scalable AI Models with PyTorch Lightning

コースを見る

演習の手順

  • Define a class LightModel that inherits from pl.LightningModule.
  • Define a linear layer to transform your input, assuming the input features are 16 and there are 10 output classes.

実践的なインタラクティブ演習

このサンプルコードを完成させて、この演習に挑戦してみましょう。

# Define the model class
class LightModel(____):
  	# Define a linear layer to transform your input
    def __init__(self):
        super().__init__()
        self.layer = ____
    def forward(self, x):
        return self.layer(x)
    def training_step(self, batch, batch_idx):
        x, y = batch
        logits = self(x)
        loss = torch.nn.functional.cross_entropy(logits, y)
        return loss
コードを編集して実行