tf.contrib.timeseries.WholeDatasetInputFn
Stay organized with collections
Save and categorize content based on your preferences.
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
View source
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__
View source
__call__()
Call self as a function.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.contrib.timeseries.WholeDatasetInputFn\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py#L578-L619) |\n\nSupports passing a full time series to a model for evaluation/inference. \n\n tf.contrib.timeseries.WholeDatasetInputFn(\n time_series_reader\n )\n\nNote that this `TimeSeriesInputFn` is not designed for high throughput, and\nshould not be used for training. It allows for sequential evaluation on a full\ndataset (with sequential in-sample predictions), which then feeds naturally\ninto `predict_continuation_input_fn` for making out-of-sample\npredictions. While this is useful for plotting and interactive use,\n`RandomWindowInputFn` is better suited to training and quantitative\nevaluation.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------------|----------------------------|\n| `time_series_reader` | A TimeSeriesReader object. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `create_batch`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py#L607-L619) \n\n create_batch()\n\nA suitable `input_fn` for an `Estimator`'s `evaluate()`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| A dictionary mapping feature names to `Tensors`, each shape prefixed by [1, data set size](/versions/r1.15/api_docs/python/tf/contrib/timeseries/i.e.%20a%20batch%20size%20of%201). ||\n\n\u003cbr /\u003e\n\n### `__call__`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py#L573-L575) \n\n __call__()\n\nCall self as a function."]]
View source on GitHub