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,
	**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`
`**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