tf.contrib.timeseries.predict_continuation_input_fn( evaluation, steps=None, times=None, exogenous_features=None )
Defined in tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py
.
An Estimator input_fn for running predict() after evaluate().
If the call to evaluate() we are making predictions based on had a batch_size greater than one, predictions will start after each of these windows (i.e. will have the same batch dimension).
evaluation
: The dictionary returned by Estimator.evaluate
, with keys FilteringResults.STATE_TUPLE and FilteringResults.TIMES.steps
: The number of steps to predict (scalar), starting after the evaluation. If times
is specified, steps
must not be; one is required.times
: A [batch_size x window_size] array of integers (not a Tensor) indicating times to make predictions for. These times must be after the corresponding evaluation. If steps
is specified, times
must not be; one is required. If the batch dimension is omitted, it is assumed to be 1.exogenous_features
: Optional dictionary. If specified, indicates exogenous features for the model to use while making the predictions. Values must have shape [batch_size x window_size x ...], where batch_size
matches the batch dimension used when creating evaluation
, and window_size
is either the steps
argument or the window_size
of the times
argument (depending on which was specified).An input_fn
suitable for passing to the predict
function of a time series Estimator
.
ValueError
: If times
or steps
are misspecified.
© 2018 The TensorFlow Authors. All rights reserved.
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/predict_continuation_input_fn