tsgm.models.architectures

Package Contents

class Sampling[source]

Bases: tensorflow.keras.layers.Layer

Custom Keras layer for sampling from a latent space.

This layer samples from a latent space using the reparameterization trick during training. It takes as input the mean and log variance of the latent distribution and generates samples by adding random noise scaled by the standard deviation to the mean.

call(inputs: Tuple[tsgm.types.Tensor, tsgm.types.Tensor]) → tsgm.types.Tensor[source]

Generates samples from a latent space.

Parameters:

inputs (tuple[tsgm.types.Tensor, tsgm.types.Tensor]) – Tuple containing mean and log variance tensors of the latent distribution.

Returns:

Sampled latent vector.

Return type:

tsgm.types.Tensor

class Architecture[source]

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

class BaseGANArchitecture[source]

Bases: Architecture

Base class for defining architectures of Generative Adversarial Networks (GANs).

property discriminator: tensorflow.keras.models.Model

Property for accessing the discriminator model.

Returns:

The discriminator model.

Return type:

keras.models.Model

Raises:

NotImplementedError – If the discriminator model is not found.

property generator: tensorflow.keras.models.Model

Property for accessing the generator model.

Returns:

The generator model.

Return type:

keras.models.Model

Raises:

NotImplementedError – If the generator model is not implemented.

get() → Dict[source]

Retrieves both discriminator and generator models as a dictionary.

Returns:

A dictionary containing discriminator and generator models.

Return type:

dict

Raises:

NotImplementedError – If either discriminator or generator models are not implemented.

class BaseVAEArchitecture[source]

Bases: Architecture

Base class for defining architectures of Variational Autoencoders (VAEs).

property encoder: tensorflow.keras.models.Model

Property for accessing the encoder model.

Returns:

The encoder model.

Return type:

keras.models.Model

Raises:

NotImplementedError – If the encoder model is not implemented.

property decoder: tensorflow.keras.models.Model

Property for accessing the decoder model.

Returns:

The decoder model.

Return type:

keras.models.Model

Raises:

NotImplementedError – If the decoder model is not implemented.

get() → Dict[source]

Retrieves both encoder and decoder models as a dictionary.

Returns:

A dictionary containing encoder and decoder models.

Return type:

dict

Raises:

NotImplementedError – If either encoder or decoder models are not implemented.

class VAE_CONV5Architecture(seq_len: int, feat_dim: int, latent_dim: int)[source]

Bases: BaseVAEArchitecture

This class defines the architecture for a Variational Autoencoder (VAE) with Convolutional Layers.

Parameters:

seq_len (int): Length of input sequence. feat_dim (int): Dimensionality of input features. latent_dim (int): Dimensionality of latent space.

Initializes the VAE_CONV5Architecture.

Parameters:

  • seq_len (int) – Length of input sequences.

  • feat_dim (int) – Dimensionality of input features.

  • latent_dim (int) – Dimensionality of latent space.

class cVAE_CONV5Architecture(seq_len: int, feat_dim: int, latent_dim: int, output_dim: int = 2)[source]

Bases: BaseVAEArchitecture

Base class for defining architectures of Variational Autoencoders (VAEs).

class cGAN_Conv4Architecture(seq_len: int, feat_dim: int, latent_dim: int, output_dim: int)[source]

Bases: BaseGANArchitecture

Architecture for Conditional Generative Adversarial Network (cGAN) with Convolutional Layers.

Initializes the cGAN_Conv4Architecture.

Parameters:

  • seq_len (int) – Length of input sequence.

  • feat_dim (int) – Dimensionality of input features.

  • latent_dim (int) – Dimensionality of latent space.

  • output_dim (int) – Dimensionality of output.

class tcGAN_Conv4Architecture(seq_len: int, feat_dim: int, latent_dim: int, output_dim: int)[source]

Bases: BaseGANArchitecture

Architecture for Temporal Conditional Generative Adversarial Network (tcGAN) with Convolutional Layers.

Initializes the tcGAN_Conv4Architecture.

Parameters:

  • seq_len (int) – Length of input sequence.

  • feat_dim (int) – Dimensionality of input features.

  • latent_dim (int) – Dimensionality of latent space.

  • output_dim (int) – Dimensionality of output.

class cGAN_LSTMConv3Architecture(seq_len: int, feat_dim: int, latent_dim: int, output_dim: int)[source]

Bases: BaseGANArchitecture

Architecture for Conditional Generative Adversarial Network (cGAN) with LSTM and Convolutional Layers.

Initializes the cGAN_LSTMConv3Architecture.

Parameters:

  • seq_len (int) – Length of input sequence.

  • feat_dim (int) – Dimensionality of input features.

  • latent_dim (int) – Dimensionality of latent space.

  • output_dim (int) – Dimensionality of output.

class BaseClassificationArchitecture(seq_len: int, feat_dim: int, output_dim: int)[source]

Bases: Architecture

Base class for classification architectures.

Parameters:

  • seq_len (int) – Length of input sequences.

  • feat_dim (int) – Dimensionality of input features.

  • output_dim (int) – Dimensionality of the output.

Initializes the base classification architecture.

Parameters:

  • seq_len (int) – Length of input sequences.

  • feat_dim (int) – Dimensionality of input features.

  • output_dim (int) – Dimensionality of the output.

property model: tensorflow.keras.models.Model

Property to access the underlying Keras model.

Returns:

The Keras model.

Return type:

keras.models.Model

get() → Dict[source]

Returns a dictionary containing the model.

Returns:

A dictionary containing the model.

Return type:

dict

class ConvnArchitecture(seq_len: int, feat_dim: int, output_dim: int, n_conv_blocks: int = 1)[source]

Bases: BaseClassificationArchitecture

Convolutional neural network architecture for classification. Inherits from BaseClassificationArchitecture.

Initializes the convolutional neural network architecture.

Parameters:

  • seq_len (int) – Length of input sequences.

  • feat_dim (int) – Dimensionality of input features.

  • output_dim (int) – Dimensionality of the output.

  • n_conv_blocks (int, optional) – Number of convolutional blocks to use (default is 1).

class ConvnLSTMnArchitecture(seq_len: int, feat_dim: int, output_dim: int, n_conv_lstm_blocks: int = 1)[source]

Bases: BaseClassificationArchitecture

Base class for classification architectures.

Parameters:

  • seq_len (int) – Length of input sequences.

  • feat_dim (int) – Dimensionality of input features.

  • output_dim (int) – Dimensionality of the output.

Initializes the base classification architecture.

Parameters:

  • seq_len (int) – Length of input sequences.

  • feat_dim (int) – Dimensionality of input features.

  • output_dim (int) – Dimensionality of the output.

class BlockClfArchitecture(seq_len: int, feat_dim: int, output_dim: int, blocks: list)[source]

Bases: BaseClassificationArchitecture

Architecture for classification using a sequence of blocks.

Inherits from BaseClassificationArchitecture.

Initializes the BlockClfArchitecture.

Parameters:

  • seq_len (int) – Length of input sequences.

  • feat_dim (int) – Dimensionality of input features.

  • output_dim (int) – Dimensionality of the output.

  • blocks (list) – List of blocks used in the architecture.

class BasicRecurrentArchitecture(hidden_dim: int, output_dim: int, n_layers: int, network_type: str, name: str = 'Sequential')[source]

Bases: Architecture

Base class for basic recurrent neural network architectures.

Inherits from Architecture.

Parameters:

  • hidden_dim – int, the number of units (e.g. 24)

  • output_dim – int, the number of output units (e.g. 1)

  • n_layers – int, the number of layers (e.g. 3)

  • network_type – str, one of ‘gru’, ‘lstm’, or ‘lstmLN’

  • name – str, model name Default: “Sequential”

_rnn_cell() → tensorflow.keras.layers.Layer[source]

Basic RNN Cell :return cell: keras.layers.Layer

build(activation: str = 'sigmoid', return_sequences: bool = True) → tensorflow.keras.models.Model[source]

Builds the recurrent neural network model.

Parameters:

  • activation (str) – Activation function for the output layer (default is ‘sigmoid’).

  • return_sequences (bool) – Whether to return the full sequence of outputs (default is True).

Returns:

The built Keras model.

Return type:

keras.models.Model

class cGAN_LSTMnArchitecture(seq_len: int, feat_dim: int, latent_dim: int, output_dim: int, n_blocks: int = 1, output_activation: str = 'tanh')[source]

Bases: BaseGANArchitecture

Conditional Generative Adversarial Network (cGAN) with LSTM-based architecture.

Inherits from BaseGANArchitecture.

Initializes the cGAN_LSTMnArchitecture.

Parameters:

  • seq_len (int) – Length of input sequences.

  • feat_dim (int) – Dimensionality of input features.

  • latent_dim (int) – Dimensionality of the latent space.

  • output_dim (int) – Dimensionality of the output.

  • n_blocks (int, optional) – Number of LSTM blocks in the architecture (default is 1).

  • output_activation (str, optional) – Activation function for the output layer (default is “tanh”).

class Zoo(*arg, **kwargs)[source]

Bases: dict

A collection of architectures represented. It behaves like supports Python dict API.

Initializes the Zoo.

summary() → None[source]

Prints a summary of architectures in the Zoo.