tf.contrib.timeseries.saved_model_utils.cold_start_filter( signatures, session, features )
Defined in tensorflow/contrib/timeseries/python/timeseries/saved_model_utils.py
.
Perform filtering using an exported saved model.
Filtering refers to updating model state based on new observations. Predictions based on the returned model state will be conditioned on these observations.
Starts from the model's default/uninformed state.
signatures
: The MetaGraphDef
protocol buffer returned from tf.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.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] (univariate) or [series length x number of features] (multivariate). 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.A dictionary containing model state updated to account for the observations in features
.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/timeseries/saved_model_utils/cold_start_filter