Predict using a Loaded SavedModel

Performs a prediction using a SavedModel model already loaded using load_savedmodel().

# S3 method for graph_prediction
predict_savedmodel(instances, model, sess,
  signature_name = "serving_default", ...)

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, CloudML name or graph object. A local path can be exported using `export_savedmodel()`, a REST URL can be created using `serve_savedmodel()`, a CloudML model can be deployed usin `cloudml::cloudml_deploy()` and a graph object loaded using `load_savedmodel()`. Notice that predicting over a CloudML model requires a `version` parameter to identify the model. A `type` parameter can be specified to explicitly choose the type model performing the prediction. Valid values are `cloudml`, `export`, `webapi` and `graph`.
sess	The active TensorFlow session.
signature_name	The named entry point to use in the model for prediction.
...	See `predict_savedmodel.export_prediction()`, `predict_savedmodel.graph_prediction()`, `predict_savedmodel.webapi_prediction()` and `predict_savedmodel.cloudml_prediction()` for additional options. #' @section Implementations: `predict_savedmodel.export_prediction()` `predict_savedmodel.graph_prediction()` `predict_savedmodel.webapi_prediction()`] `predict_savedmodel.cloudml_prediction()`