Perform filtering using an exported saved model.
tf.contrib.timeseries.saved_model_utils.filter_continuation( 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 observations.
continue_from: 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.
MetaGraphDefprotocol 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
session: The session to use. The session's graph must be the one into which
tf.compat.v1.saved_model.loader.loadloaded the model.
features: A dictionary mapping keys to Numpy arrays, with several possible shapes (requires keys
FilteringFeatures.VALUES): Single example;
TIMESis a scalar and
VALUESis either a scalar or a vector of length [number of features]. Sequence;
TIMESis a vector of shape [series length],
VALUESeither has shape series length or series length x number of features. Batch of sequences;
TIMESis a vector of shape [batch size x series length],
VALUEShas shape [batch size x series length] or [batch size x series length x number of features]. In any case,
VALUESand 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