predict_savedmodel: Predict using a SavedModel

In rstudio/tfdeploy: Deploy 'TensorFlow' Models

Description

Runs a prediction over a saved model file, web API or graph object.

Usage

predict_savedmodel(instances, model, ...)

Arguments

instances	A list of prediction instances to be passed as input tensors to the service. Even for single predictions, a list with one entry is expected.
model	The model as a local path, a REST url or graph object. A local path can be exported using export_savedmodel(), a REST URL can be created using serve_savedmodel() and a graph object loaded using load_savedmodel(). A type parameter can be specified to explicitly choose the type model performing the prediction. Valid values are export, webapi and graph.
...	See predict_savedmodel.export_prediction(), predict_savedmodel.graph_prediction(), predict_savedmodel.webapi_prediction() for additional options. #' @section Implementations: predict_savedmodel.export_prediction() predict_savedmodel.graph_prediction() predict_savedmodel.webapi_prediction()]

See Also

export_savedmodel(), serve_savedmodel(), load_savedmodel()

Examples

## Not run: 
# perform prediction based on an existing model
tfdeploy::predict_savedmodel(
  list(rep(9, 784)),
  system.file("models/tensorflow-mnist", package = "tfdeploy")
)

## End(Not run)

rstudio/tfdeploy documentation built on July 9, 2021, 1:35 a.m.