multiml.task.keras.keras_ensemble module

class multiml.task.keras.keras_ensemble.EnsembleTask(subtasks, dropout_rate=None, individual_loss=False, individual_loss_weights=0.0, **kwargs)

Bases: KerasBaseTask

__init__(subtasks, dropout_rate=None, individual_loss=False, individual_loss_weights=0.0, **kwargs)

Parameters:

• subtasks (list) – list of task instances.

• dropout_rate (float) – dropout_rate for ensemble weights. If None, no dropout.

• individual_loss (bool) – use multiple outputs

• individual_loss_weights (float) – coefficient for multiple outputs

• **kwargs – Arbitrary keyword arguments

compile()

Compile model, optimizer and loss.

Compiled objects will be avaialble via self.ml.model, self.ml.optimizer and self.ml.loss.

Examples

>>> # compile all together,
>>> self.compile()
>>> # which is equivalent to:
>>> self.build_model() # set self._model
>>> self.compile_model() # set self.ml.model
>>> self.compile_optimizer() # set self.ml.optimizer
>>> self.compile_loss() # set self.ml.loss

compile_loss()

Compile keras model.

build_model()

Build model.

get_input_true_data(phase)

Get input and true data.

Parameters:

phase (str) – data type (train, valid, test or None).

Returns:

(input, true) data for model.

Return type:

tuple

get_inputs()

Returns keras Input from input_var_names.

get_submodel_names()

Returns subtask_id used in ensembling.

Returns:

list of subtask_id

Return type:

list (str)

get_submodel(i_models)

Get a submodel by model index.

Parameters:

i_models (int) – submodel index

Returns:

submodel for the input index

Return type:

subtasktuple

static get_ensemble_weights(model)

Collect ensemble_weights in the keras model.

Parameters:

model (keras.Model) –

Returns:

list of ensemble weights

Return type:

list (tf.Variable)