tf.contrib.timeseries.saved_model_utils.filter_continuation
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 | |
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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 | |
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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/versions/r1.15/api_docs/python/tf/contrib/timeseries/saved_model_utils/filter_continuation