keras_response_all_of module

KerasResponseAllOf

class edgeimpulse_api.models.keras_response_all_of.KerasResponseAllOf(
		**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

  • akida_edge_learning_config: Optional[edgeimpulse_api.models.akida_edge_learning_config.AkidaEdgeLearningConfig]

  • anomaly_capacity: Optional[edgeimpulse_api.models.anomaly_capacity.AnomalyCapacity]

  • augmentation_policy_image: edgeimpulse_api.models.augmentation_policy_image_enum.AugmentationPolicyImageEnum

  • augmentation_policy_spectrogram: Optional[edgeimpulse_api.models.augmentation_policy_spectrogram.AugmentationPolicySpectrogram]

  • auto_class_weights: Optional[pydantic.types.StrictBool]

  • batch_size: Optional[pydantic.types.StrictInt]

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

  • custom_validation_metadata_key: Optional[pydantic.types.StrictStr]

  • default_batch_size: pydantic.types.StrictInt

  • dependencies: edgeimpulse_api.models.dependency_data.DependencyData

  • last_shown_model_engine: Optional[edgeimpulse_api.models.model_engine_short_enum.ModelEngineShortEnum]

  • last_shown_model_variant: Optional[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum]

  • learning_rate: float

  • minimum_confidence_rating: float

  • mode: pydantic.types.StrictStr

  • name: pydantic.types.StrictStr

  • profile_int8: pydantic.types.StrictBool

  • script: pydantic.types.StrictStr

  • selected_model_type: edgeimpulse_api.models.keras_model_type_enum.KerasModelTypeEnum

  • shape: pydantic.types.StrictStr

  • show_advanced_training_settings: pydantic.types.StrictBool

  • show_augmentation_training_settings: pydantic.types.StrictBool

  • skip_embeddings_and_memory: pydantic.types.StrictBool

  • train_test_split: Optional[float]

  • trained: pydantic.types.StrictBool

  • training_cycles: pydantic.types.StrictInt

  • transfer_learning_models: List[edgeimpulse_api.models.transfer_learning_model.TransferLearningModel]

  • type: Optional[edgeimpulse_api.models.learn_block_type.LearnBlockType]

  • use_learned_optimizer: Optional[pydantic.types.StrictBool]

  • visual_layers: List[edgeimpulse_api.models.keras_visual_layer.KerasVisualLayer]

Static methods

from_dict

edgeimpulse_api.models.keras_response_all_of.from_dict(
		obj: dict
)> edgeimpulse_api.models.keras_response_all_of.KerasResponseAllOf

Create an instance of KerasResponseAllOf from a dict

Parameters

  • obj: dict

Return

edgeimpulse_api.models.keras_response_all_of.KerasResponseAllOf

from_json

edgeimpulse_api.models.keras_response_all_of.from_json(
		json_str: str
)> edgeimpulse_api.models.keras_response_all_of.KerasResponseAllOf

Create an instance of KerasResponseAllOf from a JSON string

Parameters

  • json_str: str

Return

edgeimpulse_api.models.keras_response_all_of.KerasResponseAllOf

mode_validate_enum

edgeimpulse_api.models.keras_response_all_of.mode_validate_enum(
		v
)

Parameters