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.
Args |
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.
|
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.
|
features
|
A dictionary mapping keys to Numpy arrays, with several possible
shapes (requires keys FilteringFeatures.TIMES and
FilteringFeatures.VALUES ): Single example; TIMES is a scalar and
VALUES is either a scalar or a vector of length [number of features].
Sequence; TIMES is a vector of shape [series length], VALUES either
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
any case, VALUES and any exogenous features must have their shapes
prefixed by the shape of the value corresponding to the TIMES key.
|
Returns |
A dictionary containing model state updated to account for the observations
in features .
|