View source on GitHub
|
Supports passing a full time series to a model for evaluation/inference.
tf.contrib.timeseries.WholeDatasetInputFn(
time_series_reader
)
Note that this TimeSeriesInputFn is not designed for high throughput, and
should not be used for training. It allows for sequential evaluation on a full
dataset (with sequential in-sample predictions), which then feeds naturally
into predict_continuation_input_fn for making out-of-sample
predictions. While this is useful for plotting and interactive use,
RandomWindowInputFn is better suited to training and quantitative
evaluation.
Args | |
|---|---|
time_series_reader
|
A TimeSeriesReader object. |
Methods
create_batch
create_batch()
A suitable input_fn for an Estimator's evaluate().
| Returns | |
|---|---|
A dictionary mapping feature names to Tensors, each shape
prefixed by 1, data set size.
|
__call__
__call__()
Call self as a function.