multiml.agent.SequentialAgent

class multiml.agent.SequentialAgent(differentiable=None, diff_pretrain=False, diff_task_args=None, num_trials=None, **kwargs)

Agent execute sequential tasks.

Examples

>>> task0 = your_task0
>>> task1 = your_task1
>>> task2 = your_task2
>>>
>>> agent = SequentialAgent(storegate=storegate,
>>>                         task_scheduler=[task0, task1, task2],
>>>                         metric=your_metric)
>>> agent.execute()
>>> agent.finalize()

__init__(differentiable=None, diff_pretrain=False, diff_task_args=None, num_trials=None, **kwargs)

Initialize sequential agent.

Parameters:

• differentiable (str) – keras or pytorch. If differentiable is given, ConnectionTask() is created based on sequential tasks. If differentiable is None (default), sequential tasks are executed step by step.

• diff_pretrain (bool) – If True, each subtask is trained before creating ConnectionTask()`.

• diff_task_args (dict) – arbitrary args passed to ConnectionTask().

• num_trials (ine) – number of trials. Average value of trials is used as final metric.

Methods

__init__([differentiable, diff_pretrain, ...])	Initialize sequential agent.
execute()	Execute sequential agent.
execute_differentiable(subtasktuples, counter)	Execute connection model.
execute_finalize()	Execute and finalize base agent.
execute_pipeline(subtasktuples, counter[, trial])	Execute pipeline.
execute_subtasktuples(subtasktuples, counter)	Execute given subtasktuples.
finalize()	Finalize sequential agent.

Attributes

metric	Return metric of base agent.
result	Return result of execution.
saver	Return saver of base agent.
storegate	Return storegate of base agent.
task_scheduler	Return task_scheduler of base agent.

__init__(differentiable=None, diff_pretrain=False, diff_task_args=None, num_trials=None, **kwargs)

Initialize sequential agent.

Parameters:

• differentiable (str) – keras or pytorch. If differentiable is given, ConnectionTask() is created based on sequential tasks. If differentiable is None (default), sequential tasks are executed step by step.

• diff_pretrain (bool) – If True, each subtask is trained before creating ConnectionTask()`.

• diff_task_args (dict) – arbitrary args passed to ConnectionTask().

• num_trials (ine) – number of trials. Average value of trials is used as final metric.