NTMPredictor: Transforms input vectors to lower-dimesional representations.

In DyfanJones/sagemaker-r-mlframework: sagemaker machine learning developed by amazon

Transforms input vectors to lower-dimesional representations.

Description

The implementation of :meth:'~sagemaker.predictor.Predictor.predict' in this 'Predictor' requires a numpy “ndarray“ as input. The array should contain the same number of columns as the feature-dimension of the data used to fit the model this Predictor performs inference on. :meth:'predict()' returns a list of :class:'~sagemaker.amazon.record_pb2.Record' objects, one for each row in the input “ndarray“. The lower dimension vector result is stored in the “projection“ key of the “Record.label“ field.

Super classes

sagemaker.mlcore::PredictorBase -> sagemaker.mlcore::Predictor -> NTMPredictor

Methods

Public methods

• NTMPredictor$new()

• NTMPredictor$clone()

Inherited methods

• sagemaker.mlcore::Predictor$delete_endpoint()

• sagemaker.mlcore::Predictor$delete_model()

• sagemaker.mlcore::Predictor$delete_predictor()

• sagemaker.mlcore::Predictor$disable_data_capture()

• sagemaker.mlcore::Predictor$enable_data_capture()

• sagemaker.mlcore::Predictor$endpoint_context()

• sagemaker.mlcore::Predictor$format()

• sagemaker.mlcore::Predictor$list_monitors()

• sagemaker.mlcore::Predictor$predict()

• sagemaker.mlcore::Predictor$update_data_capture_config()

• sagemaker.mlcore::Predictor$update_endpoint()

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Method new()

Initialize NTMPredictor class

Usage

NTMPredictor$new(endpoint_name, sagemaker_session = NULL)

Arguments

endpoint_name

(str): Name of the Amazon SageMaker endpoint to which requests are sent.

sagemaker_session

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.

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Method clone()

The objects of this class are cloneable with this method.

Usage

NTMPredictor$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

DyfanJones/sagemaker-r-mlframework documentation built on March 18, 2022, 7:41 a.m.