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model.rnn_model

Encoder Objects​

class Encoder(nn.Module)

Encoder module of the Variational Autoencoder.

__init__​

def __init__(NUM_FEATURES: int, hidden_size_layer_1: int,
             hidden_size_layer_2: int, dropout_encoder: float)

Initialize the Encoder module.

Parameters

  • NUM_FEATURES (int): Number of input features.
  • hidden_size_layer_1 (int): Size of the first hidden layer.
  • hidden_size_layer_2 (int): Size of the second hidden layer.
  • dropout_encoder (float): Dropout rate for regularization.

forward​

def forward(inputs: torch.Tensor) -> torch.Tensor

Forward pass of the Encoder module.

Parameters

  • inputs (torch.Tensor): Input tensor of shape (batch_size, sequence_length, num_features).

Returns

  • torch.Tensor:: Encoded representation tensor of shape (batch_size, hidden_size_layer_1 * 4).

Lambda Objects​

class Lambda(nn.Module)

Lambda module for computing the latent space parameters.

__init__​

def __init__(ZDIMS: int, hidden_size_layer_1: int, softplus: bool)

Initialize the Lambda module.

Parameters

  • ZDIMS (int): Size of the latent space.
  • hidden_size_layer_1 (int): Size of the first hidden layer.
  • hidden_size_layer_2 (int, deprecated): Size of the second hidden layer.
  • softplus (bool): Whether to use softplus activation for logvar.

forward​

def forward(
        hidden: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]

Forward pass of the Lambda module.

Parameters

  • hidden (torch.Tensor): Hidden representation tensor of shape (batch_size, hidden_size_layer_1 * 4).

Returns

  • tuple[torch.Tensor, torch.Tensor, torch.Tensor]: Latent space tensor, mean tensor, logvar tensor.

Decoder Objects​

class Decoder(nn.Module)

Decoder module of the Variational Autoencoder.

__init__​

def __init__(TEMPORAL_WINDOW: int, ZDIMS: int, NUM_FEATURES: int,
             hidden_size_rec: int, dropout_rec: float)

Initialize the Decoder module.

Parameters

  • TEMPORAL_WINDOW (int): Size of the temporal window.
  • ZDIMS (int): Size of the latent space.
  • NUM_FEATURES (int): Number of input features.
  • hidden_size_rec (int): Size of the recurrent hidden layer.
  • dropout_rec (float): Dropout rate for regularization.

forward​

def forward(inputs: torch.Tensor, z: torch.Tensor) -> torch.Tensor

Forward pass of the Decoder module.

Parameters

  • inputs (torch.Tensor): Input tensor of shape (batch_size, seq_len, ZDIMS).
  • z (torch.Tensor): Latent space tensor of shape (batch_size, ZDIMS).

Returns

  • torch.Tensor:: Decoded output tensor of shape (batch_size, seq_len, NUM_FEATURES).

Decoder_Future Objects​

class Decoder_Future(nn.Module)

Decoder module for predicting future sequences.

__init__​

def __init__(TEMPORAL_WINDOW: int, ZDIMS: int, NUM_FEATURES: int,
             FUTURE_STEPS: int, hidden_size_pred: int, dropout_pred: float)

Initialize the Decoder_Future module.

Parameters

  • TEMPORAL_WINDOW (int): Size of the temporal window.
  • ZDIMS (int): Size of the latent space.
  • NUM_FEATURES (int): Number of input features.
  • FUTURE_STEPS (int): Number of future steps to predict.
  • hidden_size_pred (int): Size of the prediction hidden layer.
  • dropout_pred (float): Dropout rate for regularization.

forward​

def forward(inputs: torch.Tensor, z: torch.Tensor) -> torch.Tensor

Forward pass of the Decoder_Future module.

Parameters

  • inputs (torch.Tensor): Input tensor of shape (batch_size, seq_len, ZDIMS).
  • z (torch.Tensor): Latent space tensor of shape (batch_size, ZDIMS).

Returns

  • torch.Tensor:: Predicted future tensor of shape (batch_size, FUTURE_STEPS, NUM_FEATURES).

RNN_VAE Objects​

class RNN_VAE(nn.Module)

Variational Autoencoder module.

__init__​

def __init__(TEMPORAL_WINDOW: int, ZDIMS: int, NUM_FEATURES: int,
             FUTURE_DECODER: bool, FUTURE_STEPS: int, hidden_size_layer_1: int,
             hidden_size_layer_2: int, hidden_size_rec: int,
             hidden_size_pred: int, dropout_encoder: float, dropout_rec: float,
             dropout_pred: float, softplus: bool)

Initialize the VAE module.

Parameters

  • TEMPORAL_WINDOW (int): Size of the temporal window.
  • ZDIMS (int): Size of the latent space.
  • NUM_FEATURES (int): Number of input features.
  • FUTURE_DECODER (bool): Whether to include a future decoder.
  • FUTURE_STEPS (int): Number of future steps to predict.
  • hidden_size_layer_1 (int): Size of the first hidden layer.
  • hidden_size_layer_2 (int): Size of the second hidden layer.
  • hidden_size_rec (int): Size of the recurrent hidden layer.
  • hidden_size_pred (int): Size of the prediction hidden layer.
  • dropout_encoder (float): Dropout rate for encoder.

forward​

def forward(seq: torch.Tensor) -> tuple

Forward pass of the VAE.

Parameters

  • seq (torch.Tensor): Input sequence tensor of shape (batch_size, seq_len, NUM_FEATURES).

Returns

  • Tuple containing:: - If FUTURE_DECODER is True:
    • prediction (torch.Tensor): Reconstructed input sequence tensor.
    • future (torch.Tensor): Predicted future sequence tensor.
    • z (torch.Tensor): Latent representation tensor.
    • mu (torch.Tensor): Mean of the latent distribution tensor.
    • logvar (torch.Tensor): Log variance of the latent distribution tensor.
  • If FUTURE_DECODER is False:
    • prediction (torch.Tensor): Reconstructed input sequence tensor.
    • z (torch.Tensor): Latent representation tensor.
    • mu (torch.Tensor): Mean of the latent distribution tensor.
    • logvar (torch.Tensor): Log variance of the latent distribution tensor.