osl_dynamics.models.sage

Single-dynamic Adversarial Generator Encoder (SAGE).

Module Contents

Classes

Config	Settings for SAGE.
Model	SAGE model class.

Functions

_build_inference_model(config)
_build_generator_model(config)
_build_discriminator_model(config)

Attributes

_logger

osl_dynamics.models.sage._logger

class osl_dynamics.models.sage.Config

Bases: osl_dynamics.models.mod_base.BaseModelConfig

Settings for SAGE.

Parameters:

• model_name (str) – Model name.

• n_modes (int) – Number of modes.

• n_channels (int) – Number of channels.

• sequence_length (int) – Length of sequence passed to the inference, generative and discriminator network.

• inference_rnn (str) – RNN to use, either 'gru' or 'lstm'.

• inference_n_layers (int) – Number of layers.

• inference_n_units (int) – Number of units.

• inference_normalization (str) – Type of normalization to use. Either None, 'batch' or 'layer'.

• inference_activation (str) – Type of activation to use after normalization and before dropout. E.g. 'relu', 'elu', etc.

• inference_dropout (float) – Dropout rate.

• inference_regularizer (str) – Regularizer.

• model_rnn (str) – RNN to use, either 'gru' or 'lstm'.

• model_n_layers (int) – Number of layers.

• model_n_units (int) – Number of units.

• model_normalization (str) – Type of normalization to use. Either None, 'batch' or 'layer'.

• model_activation (str) – Type of activation to use after normalization and before dropout. E.g. 'relu', 'elu', etc.

• model_dropout (float) – Dropout rate.

• model_regularizer (str) – Regularizer.

• discriminator_rnn (str) – RNN to use, either 'gru' or 'lstm'.

• discriminator_n_layers (int) – Number of layers.

• discriminator_n_units (int) – Number of units.

• discriminator_normalization (str) – Type of normalization to use. Either None, 'batch' or 'layer'.

• discriminator_activation (str) – Type of activation to use after normalization and before dropout. E.g. 'relu', 'elu', etc.

• discriminator_dropout (float) – Dropout rate.

• discriminator_regularizer (str) – Regularizer.

• learn_means (bool) – Should we make the mean vectors for each mode trainable?

• learn_covariances (bool) – Should we make the covariance matrix for each mode trainable?

• initial_means (np.ndarray) – Initialisation for mean vectors.

• initial_covariances (np.ndarray) – Initialisation for mode covariances.

• covariances_epsilon (float) – Error added to mode covariances for numerical stability.

• means_regularizer (tf.keras.regularizers.Regularizer) – Regularizer for mean vectors.

• covariances_regularizer (tf.keras.regularizers.Regularizer) – Regularizer for covariance matrices.

• batch_size (int) – Mini-batch size.

• learning_rate (float) – Learning rate.

• n_epochs (int) – Number of training epochs.

• optimizer (str or tf.keras.optimizers.Optimizer) – Optimizer to use. 'adam' is recommended.

• multi_gpu (bool) – Should be use multiple GPUs for training?

• strategy (str) – Strategy for distributed learning.

model_name: str = 'SAGE'

inference_rnn: str = 'lstm'

inference_n_layers: int = 1

inference_n_units: int

inference_normalization: str

inference_activation: str = 'elu'

inference_dropout: float = 0.0

inference_regularizer: str

model_rnn: str = 'lstm'

model_n_layers: int = 1

model_n_units: int

model_normalization: str

model_activation: str = 'elu'

model_dropout: float = 0.0

model_regularizer: str

discriminator_rnn: str = 'lstm'

discriminator_n_layers: int = 1

discriminator_n_units: int

discriminator_normalization: str

discriminator_activation: str = 'elu'

discriminator_dropout: float = 0.0

discriminator_regularizer: str

learn_means: bool

learn_covariances: bool

initial_means: numpy.ndarray

initial_covariances: numpy.ndarray

covariances_epsilon: float

means_regularizer: tensorflow.keras.regularizers.Regularizer

covariances_regularizer: tensorflow.keras.regularizers.Regularizer

__post_init__()

validate_observation_model_parameters()

class osl_dynamics.models.sage.Model(config)

Bases: osl_dynamics.models.mod_base.ModelBase

SAGE model class.

Parameters:

config (osl_dynamics.models.sage.Config) –

config_type

build_model()

Builds a keras model for the inference, generator and discriminator model and the full SAGE model.

compile()

Compile the model.

fit(training_data, epochs=None, verbose=1)

Train the model.

Parameters:

• training_data (tf.data.Dataset or osl_dynamics.data.Data) – Training dataset.

• epochs (int, optional) – Number of epochs to train. Defaults to value in config if not passed.

• verbose (int, optional) – Should we print a progress bar?

Returns:

history – History of discriminator_loss and generator_loss.

Return type:

history

_train_discriminator(real, fake)

get_alpha(inputs, concatenate=False)

Mode mixing factors, alpha.

Parameters:

• inputs (tf.data.Dataset or osl_dynamics.data.Data) – Prediction data.

• concatenate (bool, optional) – Should we concatenate the output?

Returns:

alpha – Mode mixing factors with shape (n_sessions, n_samples, n_modes) or (n_samples, n_modes).

Return type:

list or np.ndarray

get_means()

Get the mode means.

Returns:

means – Mode means. Shape (n_modes, n_channels).

Return type:

np.ndarray

get_covariances()

Get the mode covariances.

Returns:

covariances – Mode covariances. Shape (n_modes, n_channels, n_channels).

Return type:

np.ndarary

get_means_covariances()

Get the mode means and covariances.

This is a wrapper for get_means and get_covariances.

Returns:

• means (np.ndarary) – Mode means. Shape (n_modes, n_channels).

• covariances (np.ndarray) – Mode covariances. Shape (n_modes, n_channels, n_channels).

get_observation_model_parameters()

Wrapper for get_means_covariances.

set_means(means, update_initializer=True)

Set the mode means.

Parameters:

• means (np.ndarray) – Mode means. Shape is (n_modes, n_channels).

• update_initializer (bool, optional) – Do we want to use the passed means when we re-initialize the model?

set_covariances(covariances, update_initializer=True)

Set the mode covariances.

Parameters:

• covariances (np.ndarray) – Mode covariances. Shape is (n_modes, n_channels, n_channels).

• update_initializer (bool, optional) – Do we want to use the passed covariances when we re-initialize the model?

set_means_covariances(means, covariances, update_initializer=True)

This is a wrapper for set_means and set_covariances.

set_observation_model_parameters(observation_model_parameters, update_initializer=True)

Wrapper for set_means_covariances.

sample_alpha(alpha=None)

Uses the generator to predict the prior, alpha.

Parameters:

alpha (np.ndarray, optional) – Shape must be (n_samples, n_modes).

Returns:

alpha – Predicted alpha.

Return type:

np.ndarray

osl_dynamics.models.sage._build_inference_model(config)

osl_dynamics.models.sage._build_generator_model(config)

osl_dynamics.models.sage._build_discriminator_model(config)