keras_response

edgeimpulse_api v1.0.0

edgeimpulse_api.models.keras_response module

class edgeimpulse_api.models.keras_response.KerasResponse(*, success: StrictBool, error: Optional[StrictStr] = None, dependencies: DependencyData, trained: StrictBool, name: StrictStr, type: Optional[LearnBlockType] = None, script: StrictStr, minimumConfidenceRating: float, selectedModelType: KerasModelTypeEnum, mode: StrictStr, visualLayers: List[KerasVisualLayer], trainingCycles: StrictInt, learningRate: float, shape: StrictStr, trainTestSplit: Optional[float] = None, autoClassWeights: Optional[StrictBool] = None, findLearningRate: Optional[StrictBool] = None, augmentationPolicyImage: AugmentationPolicyImageEnum, augmentationPolicySpectrogram: Optional[AugmentationPolicySpectrogram] = None, transferLearningModels: List[TransferLearningModel], profileInt8: StrictBool, skipEmbeddingsAndMemory: StrictBool, akidaEdgeLearningConfig: Optional[AkidaEdgeLearningConfig] = None, customValidationMetadataKey: Optional[StrictStr] = None)

Bases: BaseModel

class Config()

Bases: object

allow_population_by_field_name(_ = Tru_ )

validate_assignment(_ = Tru_ )

akida_edge_learning_config(: Optional[AkidaEdgeLearningConfig )

augmentation_policy_image(_: AugmentationPolicyImageEnum

augmentation_policy_spectrogram(: Optional[AugmentationPolicySpectrogram )

auto_class_weights(: Optional[StrictBool )

custom_validation_metadata_key(: Optional[StrictStr )

dependencies(_: DependencyData

error(: Optional[StrictStr )

find_learning_rate(: Optional[StrictBool )

classmethod from_dict(obj: dict)

Create an instance of KerasResponse from a dict

classmethod from_json(json_str: str)

Create an instance of KerasResponse from a JSON string

learning_rate(: floa )

minimum_confidence_rating(: floa )

mode(: StrictSt )

classmethod mode_validate_enum(v)

name(: StrictSt )

profile_int8(: StrictBoo )

script(: StrictSt )

selected_model_type(_: KerasModelTypeEnum

shape(: StrictSt )

skip_embeddings_and_memory(: StrictBoo )

success(: StrictBoo )

to_dict()

Returns the dictionary representation of the model using alias

to_json()

Returns the JSON representation of the model using alias

to_str()

Returns the string representation of the model using alias

train_test_split(: Optional[float )

trained(: StrictBoo )

training_cycles(: StrictIn )

transfer_learning_models(: List[TransferLearningModel )

type(: Optional[LearnBlockType )

visual_layers(: List[KerasVisualLayer )