timesead.models.generative.lstm_vae_gan

Classes

`LSTMVAEGANDecoder`	Base class for all neural network modules.
`LSTMVAEGANDiscriminator`	Base class for all neural network modules.
`LSTMVAEGAN`	Base class for all neural network modules.
`LSTMVAEGANTrainer`
`LSTMVAEGANAnomalyDetector`

Module Contents

class timesead.models.generative.lstm_vae_gan.LSTMVAEGANDecoder(input_dim: int, lstm_hidden_dims: List[int] = [60], latent_dim: int = 10)

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Parameters:

input_dim (int)
lstm_hidden_dims (List[int])
latent_dim (int)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

rnn

linear_mean

forward(x: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor]

Parameters:: x (torch.Tensor)
Return type:: Tuple[torch.Tensor, torch.Tensor]

class timesead.models.generative.lstm_vae_gan.LSTMVAEGANDiscriminator(input_dim: int, lstm_hidden_dims: List[int] = [60])

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Parameters:

input_dim (int)
lstm_hidden_dims (List[int])

Initialize internal Module state, shared by both nn.Module and ScriptModule.

rnn

forward(x: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor]

Parameters:: x (torch.Tensor)
Return type:: Tuple[torch.Tensor, torch.Tensor]

class timesead.models.generative.lstm_vae_gan.LSTMVAEGAN(input_dim: int, lstm_hidden_dims: List[int] = [60], latent_dim: int = 10)

Bases: timesead.models.BaseModel

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Parameters:

input_dim (int)
lstm_hidden_dims (List[int])
latent_dim (int)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

latent_dim = 10

encoder

decoder

discriminator

classifier

vae

forward(inputs: Tuple[torch.Tensor, Ellipsis]) → Tuple[torch.Tensor, Ellipsis]

Parameters:: inputs (Tuple[torch.Tensor, Ellipsis])
Return type:: Tuple[torch.Tensor, Ellipsis]

grouped_parameters() → Tuple[Iterator[inspect.Parameter], Ellipsis]

Return type:: Tuple[Iterator[inspect.Parameter], Ellipsis]

class timesead.models.generative.lstm_vae_gan.LSTMVAEGANTrainer(*args, **kwargs)

Bases: timesead.optim.trainer.Trainer

validate_batch(network: LSTMVAEGAN, val_metrics: Dict[str, Callable], b_inputs: Tuple[torch.Tensor, Ellipsis], b_targets: Tuple[torch.Tensor, Ellipsis], *args, **kwargs) → Dict[str, float]

Parameters:

network (LSTMVAEGAN)
val_metrics (Dict[str, Callable])
b_inputs (Tuple[torch.Tensor, Ellipsis])
b_targets (Tuple[torch.Tensor, Ellipsis])

Return type:

Dict[str, float]

train_batch(network: LSTMVAEGAN, losses: List[timesead.optim.loss.Loss], optimizers: List[torch.optim.Optimizer], epoch: int, num_epochs: int, b_inputs: Tuple[torch.Tensor, Ellipsis], b_targets: Tuple[torch.Tensor, Ellipsis]) → List[float]

Parameters:

network (LSTMVAEGAN)
losses (List[timesead.optim.loss.Loss])
optimizers (List[torch.optim.Optimizer])
epoch (int)
num_epochs (int)
b_inputs (Tuple[torch.Tensor, Ellipsis])
b_targets (Tuple[torch.Tensor, Ellipsis])

Return type:

List[float]

class timesead.models.generative.lstm_vae_gan.LSTMVAEGANAnomalyDetector(model: LSTMVAEGAN, alpha: float = 0.5)

Bases: timesead.models.common.AnomalyDetector

Parameters:

model (LSTMVAEGAN)
alpha (float)

model

alpha = 0.5

fit(dataset: torch.utils.data.DataLoader) → None

Parameters:: dataset (torch.utils.data.DataLoader)
Return type:: None

compute_online_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) → torch.Tensor

Parameters:: inputs (Tuple[torch.Tensor, Ellipsis])
Return type:: torch.Tensor

abstract compute_offline_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) → torch.Tensor

Parameters:: inputs (Tuple[torch.Tensor, Ellipsis])
Return type:: torch.Tensor

format_online_targets(targets: Tuple[torch.Tensor, Ellipsis]) → torch.Tensor

Parameters:: targets (Tuple[torch.Tensor, Ellipsis])
Return type:: torch.Tensor