optimize_config_response module

OptimizeConfigResponse

class edgeimpulse_api.models.optimize_config_response.OptimizeConfigResponse(
		**data: Any
)

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Parameters

  • **data: Any

Bases

  • pydantic.main.BaseModel

  • pydantic.utils.Representation

Class variables

  • Config

  • accuracy_sem: Optional[float]

  • compiler: Optional[List[pydantic.types.StrictStr]]

  • device: Optional[Dict[str, Any]]

  • early_stopping: Optional[pydantic.types.StrictBool]

  • early_stopping_improvement_bar: Optional[float]

  • early_stopping_window_size: Optional[float]

  • enable_sem: Optional[pydantic.types.StrictBool]

  • error: Optional[pydantic.types.StrictStr]

  • import_project_metrics: Optional[pydantic.types.StrictBool]

  • import_resource_metrics: Optional[pydantic.types.StrictBool]

  • initial_trials: Optional[pydantic.types.StrictInt]

  • latency_sem: Optional[float]

  • max_maccs: Optional[float]

  • min_maccs: Optional[float]

  • momf: Optional[pydantic.types.StrictBool]

  • name: Optional[pydantic.types.StrictStr]

  • notification_on_completion: Optional[pydantic.types.StrictBool]

  • num_import_project_metrics: Optional[float]

  • num_import_resource_metrics: Optional[float]

  • optimization_objectives: Optional[List[pydantic.types.StrictStr]]

  • optimization_precision: Optional[pydantic.types.StrictStr]

  • optimization_rounds: Optional[pydantic.types.StrictInt]

  • precision: Optional[List[pydantic.types.StrictStr]]

  • raw_objectives: Optional[pydantic.types.StrictStr]

  • search_space_template: Optional[edgeimpulse_api.models.optimize_config_search_space_template.OptimizeConfigSearchSpaceTemplate]

  • space: Optional[List[edgeimpulse_api.models.tuner_space_impulse.TunerSpaceImpulse]]

  • success: pydantic.types.StrictBool

  • target_device: edgeimpulse_api.models.optimize_config_target_device.OptimizeConfigTargetDevice

  • target_latency: pydantic.types.StrictInt

  • training_cycles: Optional[pydantic.types.StrictInt]

  • trials_per_optimization_round: Optional[pydantic.types.StrictInt]

  • tuner_space_options: Optional[Dict[str, List[pydantic.types.StrictStr]]]

  • tuning_algorithm: Optional[pydantic.types.StrictStr]

  • tuning_max_trials: Optional[pydantic.types.StrictInt]

  • tuning_workers: Optional[pydantic.types.StrictInt]

  • verbose_logging: Optional[pydantic.types.StrictBool]

Static methods

from_dict

edgeimpulse_api.models.optimize_config_response.from_dict(
		obj: dict
)> edgeimpulse_api.models.optimize_config_response.OptimizeConfigResponse

Create an instance of OptimizeConfigResponse from a dict

Parameters

  • obj: dict

Return

edgeimpulse_api.models.optimize_config_response.OptimizeConfigResponse

from_json

edgeimpulse_api.models.optimize_config_response.from_json(
		json_str: str
)> edgeimpulse_api.models.optimize_config_response.OptimizeConfigResponse

Create an instance of OptimizeConfigResponse from a JSON string

Parameters

  • json_str: str

Return

edgeimpulse_api.models.optimize_config_response.OptimizeConfigResponse

optimization_precision_validate_enum

edgeimpulse_api.models.optimize_config_response.optimization_precision_validate_enum(
		v
)

Parameters

  • v

tuning_algorithm_validate_enum

edgeimpulse_api.models.optimize_config_response.tuning_algorithm_validate_enum(
		v
)

Parameters

  • v

Methods

to_dict

edgeimpulse_api.models.optimize_config_response.to_dict(
		self
)

Returns the dictionary representation of the model using alias

Parameters

  • self

to_json

edgeimpulse_api.models.optimize_config_response.to_json(
		self,
		indent=None
)> str

Returns the JSON representation of the model using alias

Parameters

  • self

  • indent=None

Return

str

to_str

edgeimpulse_api.models.optimize_config_response.to_str(
		self
)> str

Returns the string representation of the model using alias

Parameters

  • self

Return

str

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