Perform prediction using an exported saved model.
tf.contrib.timeseries.saved_model_utils.predict_continuation(
continue_from, signatures, session, steps=None, times=None,
exogenous_features=None
)
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
|
The MetaGraphDef protocol buffer returned from
tf.compat.v1.saved_model.loader.load . Used to determine the names of
Tensors to feed and fetch. Must be from the same model as continue_from .
|
session
|
The session to use. The session's graph must be the one into which
tf.compat.v1.saved_model.loader.load loaded the model.
|
steps
|
The number of steps to predict (scalar), starting after the
evaluation or filtering. If times 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. If steps 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 ...], where batch_size matches
the batch dimension used when creating continue_from , and window_size
is either the steps argument or the window_size of the times
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
|
If times or steps are misspecified.
|