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Classes

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KerasConfig

edgeimpulse_api.models.keras_config.KerasConfig(
	**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
**dataAny
Bases
pydantic.v1.main.BaseModel
pydantic.v1.utils.Representation
Class variables
Config
akida_edge_learning_configedgeimpulse_api.models.akida_edge_learning_config.AkidaEdgeLearningConfig | None
anomaly_capacityedgeimpulse_api.models.anomaly_capacity.AnomalyCapacity | None
augmentation_policy_imageedgeimpulse_api.models.augmentation_policy_image_enum.AugmentationPolicyImageEnum
augmentation_policy_spectrogramedgeimpulse_api.models.augmentation_policy_spectrogram.AugmentationPolicySpectrogram | None
auto_class_weightspydantic.v1.types.StrictBool | None
batch_sizepydantic.v1.types.StrictInt | None
block_parametersedgeimpulse_api.models.block_parameters.BlockParameters | None
custom_parametersDict[str, pydantic.v1.types.StrictStr] | None
custom_validation_metadata_keypydantic.v1.types.StrictStr | None
default_batch_sizepydantic.v1.types.StrictInt
dependenciesedgeimpulse_api.models.dependency_data.DependencyData
last_shown_model_variantedgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum | None
learning_ratefloat
minimum_confidence_ratingfloat
modepydantic.v1.types.StrictStr
namepydantic.v1.types.StrictStr
profile_int8pydantic.v1.types.StrictBool
scriptpydantic.v1.types.StrictStr
selected_model_typeedgeimpulse_api.models.keras_model_type_enum.KerasModelTypeEnum
shapepydantic.v1.types.StrictStr
show_advanced_training_settingspydantic.v1.types.StrictBool
show_augmentation_training_settingspydantic.v1.types.StrictBool
skip_embeddings_and_memorypydantic.v1.types.StrictBool
thresholdsList[edgeimpulse_api.models.block_threshold.BlockThreshold]
train_test_splitfloat | None
trainedpydantic.v1.types.StrictBool
training_cyclespydantic.v1.types.StrictInt
transfer_learning_modelsList[edgeimpulse_api.models.transfer_learning_model.TransferLearningModel]
typeedgeimpulse_api.models.learn_block_type.LearnBlockType | None
use_learned_optimizerpydantic.v1.types.StrictBool | None
visual_layersList[edgeimpulse_api.models.keras_visual_layer.KerasVisualLayer]
STATIC METHODS

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from_dict

edgeimpulse_api.models.keras_config.KerasConfig.from_dict(
	obj: dict
) ‑> edgeimpulse_api.models.keras_config.KerasConfig
Create an instance of KerasConfig from a dict
Parameters
objdict
Returns
edgeimpulse_api.models.keras_config.KerasConfig

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from_json

edgeimpulse_api.models.keras_config.KerasConfig.from_json(
	json_str: str
) ‑> edgeimpulse_api.models.keras_config.KerasConfig
Create an instance of KerasConfig from a JSON string
Parameters
json_strstr
Returns
edgeimpulse_api.models.keras_config.KerasConfig

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mode_validate_enum

edgeimpulse_api.models.keras_config.KerasConfig.mode_validate_enum(
	v
)
Parameters
v
METHODS

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to_dict

edgeimpulse_api.models.keras_config.KerasConfig.to_dict(
	self
)
Returns the dictionary representation of the model using alias
Parameters
self

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to_json

edgeimpulse_api.models.keras_config.KerasConfig.to_json(
	self,
	indent=None
) ‑> str
Returns the JSON representation of the model using alias
Parameters
self
indent=None
Returns
str

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to_str

edgeimpulse_api.models.keras_config.KerasConfig.to_str(
	self
) ‑> str
Returns the string representation of the model using alias
Parameters
self
Returns
str