Perform filtering using an exported saved model.
continue_from, signatures, session, features
Filtering refers to updating model state based on new observations.
Predictions based on the returned model state will be conditioned on these
A dictionary containing the results of either an Estimator's
evaluate method or a previous filter step (cold start or continuation).
Used to determine the model state to start filtering from.
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
The session to use. The session's graph must be the one into which
tf.compat.v1.saved_model.loader.load loaded the model.
A dictionary mapping keys to Numpy arrays, with several possible
shapes (requires keys
FilteringFeatures.VALUES): Single example;
TIMES is a scalar and
VALUES is either a scalar or a vector of length [number of features].
TIMES is a vector of shape [series length],
has shape series length or series length x number of
features. Batch of sequences;
TIMES is a vector of
shape [batch size x series length],
VALUES has shape [batch size x
series length] or [batch size x series length x number of features]. In
VALUES and any exogenous features must have their shapes
prefixed by the shape of the value corresponding to the
A dictionary containing model state updated to account for the observations