tune_tensorflow

tune_tensorflow.objective

class KerasObjective(type_dict)

Bases: tune.iterative.objective.IterativeObjectiveFunc

Parameters

type_dict (Dict[str, Type[tune_tensorflow.spec.KerasTrainingSpec]]) –

Return type

None

copy()

Return type

tune_tensorflow.objective.KerasObjective

finalize()

Return type

None

generate_sort_metric(value)

Parameters

value (float) –

Return type

float

initialize()

Return type

None

load_checkpoint(fs)

Parameters

fs (fs.base.FS) –

Return type

None

property model: keras.engine.training.Model

run_single_rung(budget)

Parameters

budget (float) –

Return type

tune.concepts.flow.report.TrialReport

save_checkpoint(fs)

Parameters

fs (fs.base.FS) –

Return type

None

property spec: tune_tensorflow.spec.KerasTrainingSpec

tune_tensorflow.spec

class KerasTrainingSpec(params, dfs)

Bases: object

Parameters

• params (Any) –

• dfs (Dict[str, Any]) –

compile_model(**add_kwargs)

Parameters

add_kwargs (Any) –

Return type

keras.engine.training.Model

compute_sort_metric(**add_kwargs)

Parameters

add_kwargs (Any) –

Return type

float

property dfs: Dict[str, Any]

finalize()

Return type

None

fit(**add_kwargs)

Parameters

add_kwargs (Any) –

Return type

keras.callbacks.History

generate_sort_metric(metric)

Parameters

metric (float) –

Return type

float

get_compile_params()

Return type

Dict[str, Any]

get_fit_metric(history)

Parameters

history (keras.callbacks.History) –

Return type

float

get_fit_params()

Return type

Tuple[List[Any], Dict[str, Any]]

get_model()

Return type

keras.engine.training.Model

load_checkpoint(fs, model)

Parameters

• fs (fs.base.FS) –

• model (keras.engine.training.Model) –

Return type

None

property params: tune.concepts.space.parameters.TuningParametersTemplate

save_checkpoint(fs, model)

Parameters

• fs (fs.base.FS) –

• model (keras.engine.training.Model) –

Return type

None

tune_tensorflow.suggest

suggest_keras_models_by_continuous_asha(space, plan, train_df=None, temp_path='', partition_keys=None, top_n=1, monitor=None, execution_engine=None, execution_engine_conf=None)

Parameters

• space (tune.concepts.space.spaces.Space) –

• plan (List[Tuple[float, int]]) –

• train_df (Optional[Any]) –

• temp_path (str) –

• partition_keys (Optional[List[str]]) –

• top_n (int) –

• monitor (Optional[Any]) –

• execution_engine (Optional[Any]) –

• execution_engine_conf (Optional[Any]) –

Return type

List[tune.concepts.flow.report.TrialReport]

suggest_keras_models_by_hyperband(space, plans, train_df=None, temp_path='', partition_keys=None, top_n=1, monitor=None, distributed=None, execution_engine=None, execution_engine_conf=None)

Parameters

• space (tune.concepts.space.spaces.Space) –

• plans (List[List[Tuple[float, int]]]) –

• train_df (Optional[Any]) –

• temp_path (str) –

• partition_keys (Optional[List[str]]) –

• top_n (int) –

• monitor (Optional[Any]) –

• distributed (Optional[bool]) –

• execution_engine (Optional[Any]) –

• execution_engine_conf (Optional[Any]) –

Return type

List[tune.concepts.flow.report.TrialReport]

suggest_keras_models_by_sha(space, plan, train_df=None, temp_path='', partition_keys=None, top_n=1, monitor=None, distributed=None, execution_engine=None, execution_engine_conf=None)

Parameters

• space (tune.concepts.space.spaces.Space) –

• plan (List[Tuple[float, int]]) –

• train_df (Optional[Any]) –

• temp_path (str) –

• partition_keys (Optional[List[str]]) –

• top_n (int) –

• monitor (Optional[Any]) –

• distributed (Optional[bool]) –

• execution_engine (Optional[Any]) –

• execution_engine_conf (Optional[Any]) –

Return type

List[tune.concepts.flow.report.TrialReport]

tune_tensorflow.utils

extract_keras_spec(params, type_dict)

Parameters

• params (tune.concepts.space.parameters.TuningParametersTemplate) –

• type_dict (Dict[str, Any]) –

Return type

Type[tune_tensorflow.spec.KerasTrainingSpec]

keras_space(model, **params)

Parameters

• model (Any) –

• params (Any) –

Return type

tune.concepts.space.spaces.Space

to_keras_spec(obj)

Parameters

obj (Any) –

Return type

Type[tune_tensorflow.spec.KerasTrainingSpec]

to_keras_spec_expr(spec)

Parameters

spec (Any) –

Return type

str