edgeimpulse_api.api.learn_api

Classes

LearnApi

edgeimpulse_api.api.learn_api.LearnApi(
	api_client=None
)

Parameters
`api_client=None`

METHODS

anomaly_trained_features

edgeimpulse_api.api.learn_api.LearnApi.anomaly_trained_features(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	feature_ax1: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Feature axis 1')],
	feature_ax2: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Feature axis 2')],
	**kwargs
) ‑> edgeimpulse_api.models.anomaly_trained_features_response.AnomalyTrainedFeaturesResponse

Trained features Get a sample of trained features, this extracts a number of samples and their features.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`feature_ax1`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Feature axis 1')]`
`feature_ax2`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Feature axis 2')]`
`**kwargs`

Returns
`edgeimpulse_api.models.anomaly_trained_features_response.AnomalyTrainedFeaturesResponse`

anomaly_trained_features_per_sample

edgeimpulse_api.api.learn_api.LearnApi.anomaly_trained_features_per_sample(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	sample_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')],
	**kwargs
) ‑> edgeimpulse_api.models.anomaly_trained_features_response.AnomalyTrainedFeaturesResponse

Trained features for sample Get trained features for a single sample. This runs both the DSP prerequisites and the anomaly classifier.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`sample_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')]`
`**kwargs`

Returns
`edgeimpulse_api.models.anomaly_trained_features_response.AnomalyTrainedFeaturesResponse`

download_keras_data

edgeimpulse_api.api.learn_api.LearnApi.download_keras_data(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	**kwargs
) ‑> str

Download Keras data export Download the data of an exported Keras block - needs to be exported via ‘exportKerasBlockData’ first

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`**kwargs`

Returns
`str`

download_keras_export

edgeimpulse_api.api.learn_api.LearnApi.download_keras_export(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	**kwargs
) ‑> str

Download Keras export Download an exported Keras block - needs to be exported via ‘exportKerasBlock’ first

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`**kwargs`

Returns
`str`

download_learn_model

edgeimpulse_api.api.learn_api.LearnApi.download_learn_model(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	model_download_id: Annotated[str, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Model download ID, which can be obtained from the project information')],
	**kwargs
) ‑> str

Download trained model Download a trained model for a learning block. Depending on the block this can be a TensorFlow model, or the cluster centroids.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`model_download_id`	`Annotated[str, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Model download ID, which can be obtained from the project information')]`
`**kwargs`

Returns
`str`

download_pretrained_model

edgeimpulse_api.api.learn_api.LearnApi.download_pretrained_model(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	pretrained_model_download_type: Annotated[str, Strict(strict=True)],
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	**kwargs
) ‑> str

Download pretrained model Download a pretrained model file

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`pretrained_model_download_type`	`Annotated[str, Strict(strict=True)]`
`impulse_id`	`Annotated[Annotated[int, Strict(strict=True)] \| None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None`
`**kwargs`

Returns
`str`

get_anomaly

edgeimpulse_api.api.learn_api.LearnApi.get_anomaly(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	**kwargs
) ‑> edgeimpulse_api.models.anomaly_config_response.AnomalyConfigResponse

Anomaly information Get information about an anomaly block, such as its dependencies. Use the impulse blocks to find the learnId.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`**kwargs`

Returns
`edgeimpulse_api.models.anomaly_config_response.AnomalyConfigResponse`

get_anomaly_metadata

edgeimpulse_api.api.learn_api.LearnApi.get_anomaly_metadata(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	**kwargs
) ‑> edgeimpulse_api.models.anomaly_model_metadata_response.AnomalyModelMetadataResponse

Anomaly metadata Get metadata about a trained anomaly block. Use the impulse blocks to find the learnId.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`**kwargs`

Returns
`edgeimpulse_api.models.anomaly_model_metadata_response.AnomalyModelMetadataResponse`

get_gmm_metadata

edgeimpulse_api.api.learn_api.LearnApi.get_gmm_metadata(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	**kwargs
) ‑> edgeimpulse_api.models.anomaly_gmm_metadata_response.AnomalyGmmMetadataResponse

Anomaly GMM metadata Get raw model metadata of the Gaussian mixture model (GMM) for a trained anomaly block. Use the impulse blocks to find the learnId.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`**kwargs`

Returns
`edgeimpulse_api.models.anomaly_gmm_metadata_response.AnomalyGmmMetadataResponse`

get_keras

edgeimpulse_api.api.learn_api.LearnApi.get_keras(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	**kwargs
) ‑> edgeimpulse_api.models.keras_response.KerasResponse

Keras information Get information about a Keras block, such as its dependencies. Use the impulse blocks to find the learnId.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`**kwargs`

Returns
`edgeimpulse_api.models.keras_response.KerasResponse`

get_keras_data_explorer_features

edgeimpulse_api.api.learn_api.LearnApi.get_keras_data_explorer_features(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	**kwargs
) ‑> edgeimpulse_api.models.get_data_explorer_features_response.GetDataExplorerFeaturesResponse

Get data explorer features t-SNE2 output of the raw dataset using embeddings from this Keras block

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`**kwargs`

Returns
`edgeimpulse_api.models.get_data_explorer_features_response.GetDataExplorerFeaturesResponse`

get_keras_metadata

edgeimpulse_api.api.learn_api.LearnApi.get_keras_metadata(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	exclude_labels: Annotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='If set to "true", the "labels" field is left empty (which can be big on e.g. regression projects).')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.keras_model_metadata_response.KerasModelMetadataResponse

Keras metadata Get metadata about a trained Keras block. Use the impulse blocks to find the learnId.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`exclude_labels`	`Annotated[Annotated[bool, Strict(strict=True)] \| None, FieldInfo(annotation=NoneType, required=True, description='If set to "true", the "labels" field is left empty (which can be big on e.g. regression projects).')] = None`
`**kwargs`

Returns
`edgeimpulse_api.models.keras_model_metadata_response.KerasModelMetadataResponse`

get_learn_x_data

edgeimpulse_api.api.learn_api.LearnApi.get_learn_x_data(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	**kwargs
) ‑> str

Download data Download the processed data for this learning block. This is data already processed by the signal processing blocks.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`**kwargs`

Returns
`str`

get_learn_y_data

edgeimpulse_api.api.learn_api.LearnApi.get_learn_y_data(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	**kwargs
) ‑> str

Download labels Download the labels for this learning block. This is data already processed by the signal processing blocks. Not all blocks support this function. If so, a GenericApiResponse is returned with an error message.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`**kwargs`

Returns
`str`

get_pretrained_model_info

edgeimpulse_api.api.learn_api.LearnApi.get_pretrained_model_info(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.get_pretrained_model_response.GetPretrainedModelResponse

Get pretrained model Receive info back about the earlier uploaded pretrained model (via uploadPretrainedModel) input/output tensors. If you want to deploy a pretrained model from the API, see startDeployPretrainedModelJob.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`impulse_id`	`Annotated[Annotated[int, Strict(strict=True)] \| None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None`
`**kwargs`

Returns
`edgeimpulse_api.models.get_pretrained_model_response.GetPretrainedModelResponse`

profile_pretrained_model

edgeimpulse_api.api.learn_api.LearnApi.profile_pretrained_model(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.start_job_response.StartJobResponse

Profile pretrained model Returns the latency, RAM and ROM used for the pretrained model - upload first via uploadPretrainedModel. This is using the project’s selected latency device. Updates are streamed over the websocket API (or can be retrieved through the /stdout endpoint). Use getProfileTfliteJobResult to get the results when the job is completed.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`impulse_id`	`Annotated[Annotated[int, Strict(strict=True)] \| None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None`
`**kwargs`

Returns
`edgeimpulse_api.models.start_job_response.StartJobResponse`

save_pretrained_model_parameters

edgeimpulse_api.api.learn_api.LearnApi.save_pretrained_model_parameters(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	save_pretrained_model_request: edgeimpulse_api.models.save_pretrained_model_request.SavePretrainedModelRequest,
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.generic_api_response.GenericApiResponse

Save parameters for pretrained model Save input / model configuration for a pretrained model. This overrides the current impulse. If you want to deploy a pretrained model from the API, see startDeployPretrainedModelJob.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`save_pretrained_model_request`	`edgeimpulse_api.models.save_pretrained_model_request.SavePretrainedModelRequest`
`impulse_id`	`Annotated[Annotated[int, Strict(strict=True)] \| None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None`
`**kwargs`

Returns
`edgeimpulse_api.models.generic_api_response.GenericApiResponse`

set_anomaly

edgeimpulse_api.api.learn_api.LearnApi.set_anomaly(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	set_anomaly_parameter_request: edgeimpulse_api.models.set_anomaly_parameter_request.SetAnomalyParameterRequest,
	**kwargs
) ‑> edgeimpulse_api.models.generic_api_response.GenericApiResponse

Anomaly settings Configure the anomaly block, such as its minimum confidence score. Use the impulse blocks to find the learnId.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`set_anomaly_parameter_request`	`edgeimpulse_api.models.set_anomaly_parameter_request.SetAnomalyParameterRequest`
`**kwargs`

Returns
`edgeimpulse_api.models.generic_api_response.GenericApiResponse`

set_keras

edgeimpulse_api.api.learn_api.LearnApi.set_keras(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	set_keras_parameter_request: edgeimpulse_api.models.set_keras_parameter_request.SetKerasParameterRequest,
	**kwargs
) ‑> edgeimpulse_api.models.generic_api_response.GenericApiResponse

Keras settings Configure the Keras block, such as its minimum confidence score. Use the impulse blocks to find the learnId.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`set_keras_parameter_request`	`edgeimpulse_api.models.set_keras_parameter_request.SetKerasParameterRequest`
`**kwargs`

Returns
`edgeimpulse_api.models.generic_api_response.GenericApiResponse`

start_anomaly_profile_job

edgeimpulse_api.api.learn_api.LearnApi.start_anomaly_profile_job(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	**kwargs
) ‑> edgeimpulse_api.models.generic_api_response.GenericApiResponse

Start a profile job for an anomaly learn block Starts an asynchronous profiling job, if there’s no profiling information for the currently selected latency device. Afterwards, re-fetch model metadata to get the profiling job IDs.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`**kwargs`

Returns
`edgeimpulse_api.models.generic_api_response.GenericApiResponse`

start_keras_profile_job

edgeimpulse_api.api.learn_api.LearnApi.start_keras_profile_job(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	learn_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')],
	**kwargs
) ‑> edgeimpulse_api.models.generic_api_response.GenericApiResponse

Start a profile job for a Keras learn block Starts an asynchronous profiling job, if there’s no profiling information for the currently selected latency device. Afterwards, re-fetch model metadata to get the profiling job IDs.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`learn_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Learn Block ID, use the impulse functions to retrieve the ID')]`
`**kwargs`

Returns
`edgeimpulse_api.models.generic_api_response.GenericApiResponse`

test_pretrained_model

edgeimpulse_api.api.learn_api.LearnApi.test_pretrained_model(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	test_pretrained_model_request: edgeimpulse_api.models.test_pretrained_model_request.TestPretrainedModelRequest,
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.test_pretrained_model_response.TestPretrainedModelResponse

Test pretrained model Test out a pretrained model (using raw features) - upload first via uploadPretrainedModel. If you want to deploy a pretrained model from the API, see startDeployPretrainedModelJob.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`test_pretrained_model_request`	`edgeimpulse_api.models.test_pretrained_model_request.TestPretrainedModelRequest`
`impulse_id`	`Annotated[Annotated[int, Strict(strict=True)] \| None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None`
`**kwargs`

Returns
`edgeimpulse_api.models.test_pretrained_model_response.TestPretrainedModelResponse`

test_pretrained_model_images

edgeimpulse_api.api.learn_api.LearnApi.test_pretrained_model_images(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	test_pretrained_model_images_request: edgeimpulse_api.models.test_pretrained_model_images_request.TestPretrainedModelImagesRequest,
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.test_pretrained_model_response.TestPretrainedModelResponse

Test pretrained model using image data Test out a pretrained model (using image data) - upload first via uploadPretrainedModel. If you want to deploy a pretrained model from the API, see startDeployPretrainedModelJob. This will transform raw image data (e.g. RGB to grayscale, resize) before classifying. To classify raw features, see testPretrainedModel.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`test_pretrained_model_images_request`	`edgeimpulse_api.models.test_pretrained_model_images_request.TestPretrainedModelImagesRequest`
`impulse_id`	`Annotated[Annotated[int, Strict(strict=True)] \| None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None`
`**kwargs`

Returns
`edgeimpulse_api.models.test_pretrained_model_response.TestPretrainedModelResponse`

upload_pretrained_model

edgeimpulse_api.api.learn_api.LearnApi.upload_pretrained_model(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	model_file: Annotated[str, Strict(strict=True)],
	model_file_name: Annotated[str, Strict(strict=True)],
	model_file_type: Annotated[str, Strict(strict=True)],
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	representative_features: Annotated[str, Strict(strict=True)] | None = None,
	device: Annotated[Annotated[str, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='MCU used for calculating latency, query `latencyDevices` in `listProject` for a list of supported devices (and use the \\"mcu\\" property here). If this is kept empty then we\'ll show an overview of multiple devices.')] = None,
	override_input_shape: Annotated[List[Annotated[int, Strict(strict=True)]] | None, FieldInfo(annotation=NoneType, required=True, description="Optional for ONNX files: overrides the input shape of the model. This is highly suggested if the model has dynamic dimensions. If this field is not set, then all dynamic dimensions will be set to '1'.")] = None,
	**kwargs
) ‑> edgeimpulse_api.models.start_job_response.StartJobResponse

Upload a pretrained model Upload a pretrained model and receive info back about the input/output tensors. If you want to deploy a pretrained model from the API, see startDeployPretrainedModelJob.

Parameters
`self`
`project_id`	`Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]`
`model_file`	`Annotated[str, Strict(strict=True)]`
`model_file_name`	`Annotated[str, Strict(strict=True)]`
`model_file_type`	`Annotated[str, Strict(strict=True)]`
`impulse_id`	`Annotated[Annotated[int, Strict(strict=True)] \| None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None`
`representative_features`	`Annotated[str, Strict(strict=True)] \| None = None`
`device`	`Annotated[Annotated[str, Strict(strict=True)] \| None, FieldInfo(annotation=NoneType, required=True, description='MCU used for calculating latency, query` latencyDevices `in` listProject `for a list of supported devices (and use the \\"mcu\\" property here). If this is kept empty then we\'ll show an overview of multiple devices.')] = None`
`override_input_shape`	`Annotated[List[Annotated[int, Strict(strict=True)]] \| None, FieldInfo(annotation=NoneType, required=True, description="Optional for ONNX files`
`**kwargs`

Returns
`edgeimpulse_api.models.start_job_response.StartJobResponse`

OVERVIEW

CLIs

LIBRARIES

SPECIFICATIONS

PROTOCOLS

Classes

LearnApi

anomaly_trained_features

anomaly_trained_features_per_sample

download_keras_data

download_keras_export

download_learn_model

download_pretrained_model

get_anomaly

get_anomaly_metadata

get_gmm_metadata

get_keras

get_keras_data_explorer_features

get_keras_metadata

get_learn_x_data

get_learn_y_data

get_pretrained_model_info

profile_pretrained_model

save_pretrained_model_parameters

set_anomaly

set_keras

start_anomaly_profile_job

start_keras_profile_job

test_pretrained_model

test_pretrained_model_images

upload_pretrained_model

OVERVIEW

CLIs

LIBRARIES

SPECIFICATIONS

PROTOCOLS

​Classes

​LearnApi

​anomaly_trained_features

​anomaly_trained_features_per_sample

​download_keras_data

​download_keras_export

​download_learn_model

​download_pretrained_model

​get_anomaly

​get_anomaly_metadata

​get_gmm_metadata

​get_keras

​get_keras_data_explorer_features

​get_keras_metadata

​get_learn_x_data

​get_learn_y_data

​get_pretrained_model_info

​profile_pretrained_model

​save_pretrained_model_parameters

​set_anomaly

​set_keras

​start_anomaly_profile_job

​start_keras_profile_job

​test_pretrained_model

​test_pretrained_model_images

​upload_pretrained_model

Classes

LearnApi

anomaly_trained_features

anomaly_trained_features_per_sample

download_keras_data

download_keras_export

download_learn_model

download_pretrained_model

get_anomaly

get_anomaly_metadata

get_gmm_metadata

get_keras

get_keras_data_explorer_features

get_keras_metadata

get_learn_x_data

get_learn_y_data

get_pretrained_model_info

profile_pretrained_model

save_pretrained_model_parameters

set_anomaly

set_keras

start_anomaly_profile_job

start_keras_profile_job

test_pretrained_model

test_pretrained_model_images

upload_pretrained_model