tf.contrib.timeseries.saved_model_utils.predict_continuation(
continue_from,
signatures,
session,
steps=None,
times=None,
exogenous_features=None
)
Defined in tensorflow/contrib/timeseries/python/timeseries/saved_model_utils.py
.
Perform prediction using an exported saved model.
Analogous to _input_pipeline.predict_continuation_input_fn, but operates on a saved model rather than feeding into Estimator's predict method.
Args:
continue_from
: A dictionary containing the results of either an Estimator's evaluate method or filter_continuation. Used to determine the model state to make predictions starting from.signatures
: TheMetaGraphDef
protocol buffer returned fromtf.saved_model.loader.load
. Used to determine the names of Tensors to feed and fetch. Must be from the same model ascontinue_from
.session
: The session to use. The session's graph must be the one into whichtf.saved_model.loader.load
loaded the model.steps
: The number of steps to predict (scalar), starting after the evaluation or filtering. Iftimes
is specified,steps
must not be; one is required.times
: A [batch_size x window_size] array of integers (not a Tensor) indicating times to make predictions for. These times must be after the corresponding evaluation or filtering. Ifsteps
is specified,times
must not be; one is required. If the batch dimension is omitted, it is assumed to be 1.exogenous_features
: Optional dictionary. If specified, indicates exogenous features for the model to use while making the predictions. Values must have shape [batch_size x window_size x ...], wherebatch_size
matches the batch dimension used when creatingcontinue_from
, andwindow_size
is either thesteps
argument or thewindow_size
of thetimes
argument (depending on which was specified).
Returns:
A dictionary with model-specific predictions (typically having keys "mean" and "covariance") and a feature_keys.PredictionResults.TIMES key indicating the times for which the predictions were computed.
Raises:
ValueError
: Iftimes
orsteps
are misspecified.