osl_dynamics.models.mod_base

Base class for models.

Module Contents

Classes

BaseModelConfig	Base class for settings for all models.
ModelBase	Base class for all models.

Attributes

_logger

osl_dynamics.models.mod_base._logger

class osl_dynamics.models.mod_base.BaseModelConfig

Base class for settings for all models.

model_name: str

multiple_dynamics: bool = False

batch_size: int

learning_rate: float

lr_decay: float = 0.1

gradient_clip: float

n_epochs: int

optimizer: tensorflow.keras.optimizers.Optimizer = 'adam'

multi_gpu: bool = False

strategy: str

n_modes: int

n_states: int

n_channels: int

sequence_length: int

validate_training_parameters()

validate_dimension_parameters()

class osl_dynamics.models.mod_base.ModelBase(config)

Base class for all models.

Acts as a wrapper for a standard Keras model.

property is_multi_gpu

Returns True if the model’s strategy is MirroredStrategy.

osld_version

config_type

__getattr__(attr)

abstract build_model()

Build a keras model.

compile(optimizer=None)

Compile the model.

Parameters:

optimizer (str or tf.keras.optimizers.Optimizer) – Optimizer to use when compiling.

fit(*args, use_tqdm=False, tqdm_class=None, save_best_after=None, save_filepath=None, **kwargs)

Wrapper for the standard keras fit method.

Adds callbacks and then trains the model.

Parameters:

• args (arguments) – Arguments for keras.Model.fit().

• use_tqdm (bool, optional) – Should we use a tqdm progress bar instead of the usual output from tensorflow.

• tqdm_class (TqdmCallback, optional) – Class for the tqdm progress bar.

• save_best_after (int, optional) – Epoch number after which we should save the best model. The best model is that which achieves the lowest loss.

• save_filepath (str, optional) – Path to save the best model to.

• additional_callbacks (list, optional) – List of keras callback objects.

• kwargs (keyword arguments, optional) – Keyword arguments for keras.Model.fit().

Returns:

history – The training history.

Return type:

history

load_weights(filepath)

Load weights of the model from a file.

Parameters:

filepath (str) – Path to file containing model weights.

reset_weights(keep=None)

Resets trainable variables in the model to their initial value.

reset()

Reset the model as if you’ve built a new model.

make_dataset(inputs, shuffle=False, concatenate=False, step_size=None, drop_last_batch=False)

Converts a Data object into a TensorFlow Dataset.

Parameters:

• inputs (osl_dynamics.data.Data or str or np.ndarray) – Data object. If a str or :np.ndarray: is passed this function will first convert it into a Data object.

• shuffle (bool, optional) – Should we shuffle the data?

• concatenate (bool, optional) – Should we return a single TensorFlow Dataset or a list of Datasets.

• step_size (int, optional) – Number of samples to slide the sequence across the dataset. Default is no overlap.

• drop_last_batch (bool, optional) – Should we drop the last batch if it is smaller than the batch size?

Returns:

dataset – TensorFlow Dataset (or list of Datasets) that can be used for training/evaluating.

Return type:

tf.data.Dataset or list

get_training_time_series(training_data, prepared=True, concatenate=False)

Get the time series used for training from a Data object.

Parameters:

• training_data (osl_dynamics.data.Data) – Data object.

• prepared (bool, optional) – Should we return the prepared data? If not, we return the raw data.

• concatenate (bool, optional) – Should we concatenate the data for each session?

Returns:

training_data – Training data time series.

Return type:

np.ndarray or list

summary_string()

Return a summary of the model as a string.

This is a modified version of the keras.Model.summary() method which makes the output easier to parse.

summary_table(renderer)

Return a summary of the model as a table (HTML or LaTeX).

Parameters:

renderer (str) – Renderer to use. Either "html" or "latex".

Returns:

table – Summary of the model as a table.

Return type:

str

html_summary()

Return a summary of the model as an HTML table.

latex_summary()

Return a summary of the model as a LaTeX table.

_repr_html_()

Display the model as an HTML table in Jupyter notebooks.

This is called when you type the variable name of the model in a Jupyter notebook. It is unlikely that you will need to call this.

save_config(dirname)

Saves config object as a .yml file.

Parameters:

dirname (str) – Directory to save config.yml.

save(dirname)

Saves config object and weights of the model.

This is a wrapper for self.save_config and self.save_weights.

Parameters:

dirname (str) – Directory to save the config object and weights of the model.

set_static_loss_scaling_factor(dataset)

Set the n_batches attribute of the "static_loss_scaling_factor" layer.

Parameters:

dataset (tf.data.Dataset) – TensorFlow dataset.

Note

This assumes every model has a layer called "static_loss_scaling_factor", with an attribure called "n_batches".

set_trainable(layers, values)

Context manager to temporarily set the trainable attribute of layers.

Parameters:

• layers (str or list of str) – List of layers to set the trainable attribute of.

• values (bool or list of bool) – Value to set the trainable attribute of the layers to.

static load_config(dirname)

Load a config object from a .yml file.

Parameters:

dirname (str) – Directory to load config.yml from.

Returns:

• config (dict) – Dictionary containing values used to create the config object.

• version (str) – Version used to train the model.

classmethod load(dirname, single_gpu=True)

Load model from dirname.

Parameters:

• dirname (str) – Directory where config.yml and weights are stored.

• single_gpu (bool, optional) – Should we compile the model on a single GPU?

Returns:

model – Model object.

Return type:

Model