Autoencoder

The LitAutoEncoder is a PyTorch Lightning module designed for unsupervised learning tasks. It consists of an encoder and a decoder network.

Key Features

• Encoder: Compresses input data into a latent representation.
• Decoder: Reconstructs the input data from the latent representation.
• Loss Function: Mean Squared Error (MSE) is used to measure reconstruction quality.

Autoencoder Class API

uv_datascience_project_template.lit_auto_encoder

LitAutoEncoder(encoder, decoder)

Bases: LightningModule

A simple autoencoder model.

PARAMETER	DESCRIPTION
encoder	The encoder component, responsible for encoding input data. TYPE: Sequential
decoder	The decoder component, responsible for decoding encoded data. TYPE: Sequential

Source code in src/uv_datascience_project_template/lit_auto_encoder.py

def __init__(self, encoder: nn.Sequential, decoder: nn.Sequential) -> None:
    super().__init__()
    self.encoder = encoder
    self.decoder = decoder

configure_optimizers()

Configure the Adam optimizer.

Source code in src/uv_datascience_project_template/lit_auto_encoder.py

def configure_optimizers(self) -> optim.Adam:
    """Configure the Adam optimizer."""
    optimizer = optim.Adam(self.parameters(), lr=1e-3)
    return optimizer

training_step(batch, batch_idx)

Performs a single training step for the model.

PARAMETER	DESCRIPTION
batch	A tuple containing the input data (x) and the corresponding labels (y). TYPE: Tuple[Tensor, Tensor]
batch_idx	The index of the current batch. TYPE: int

RETURNS	DESCRIPTION
Tensor	The computed loss for the current training step. TYPE: Tensor

Source code in src/uv_datascience_project_template/lit_auto_encoder.py

def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
    """Performs a single training step for the model.

    Args:
        batch (Tuple[Tensor, Tensor]): A tuple containing the input data (x) and
            the corresponding labels (y).
        batch_idx (int): The index of the current batch.

    Returns:
        Tensor: The computed loss for the current training step.
    """

    x, y = batch
    x = x.view(x.size(0), -1)
    z = self.encoder(x)
    x_hat = self.decoder(z)
    loss = nn.functional.mse_loss(x_hat, x)
    # Logging to TensorBoard (if installed) by default
    # self.log("train_loss", loss)
    return loss