tf.contrib.timeseries.NumpyReader
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A time series parser for feeding Numpy arrays to a TimeSeriesInputFn.
tf.contrib.timeseries.NumpyReader(
data, read_num_records_hint=4096
)
Avoids embedding data in the graph as constants.
Args |
|---|
data
|
A dictionary mapping feature names to Numpy arrays, with two
possible shapes (requires keys TrainEvalFeatures.TIMES and
TrainEvalFeatures.VALUES): Univariate; TIMES and VALUES are both
vectors of shape [series length] Multivariate; TIMES is a vector of
shape [series length], VALUES has shape [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.
|
read_num_records_hint
|
The maximum number of samples to read at one time,
for efficiency.
|
Methods
check_dataset_size
View source
check_dataset_size(
minimum_dataset_size
)
Raise an error if the dataset is too small.
read
View source
read()
Returns a large chunk of the Numpy arrays for later re-chunking.
read_full
View source
read_full()
Returns Tensor versions of the full Numpy arrays.
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.contrib.timeseries.NumpyReader\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#L223-L286) |\n\nA time series parser for feeding Numpy arrays to a `TimeSeriesInputFn`. \n\n tf.contrib.timeseries.NumpyReader(\n data, read_num_records_hint=4096\n )\n\nAvoids embedding data in the graph as constants.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `data` | A dictionary mapping feature names to Numpy arrays, with two possible shapes (requires keys `TrainEvalFeatures.TIMES` and `TrainEvalFeatures.VALUES`): Univariate; `TIMES` and `VALUES` are both vectors of shape \\[series length\\] Multivariate; `TIMES` is a vector of shape \\[series length\\], `VALUES` has shape \\[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. |\n| `read_num_records_hint` | The maximum number of samples to read at one time, for efficiency. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `check_dataset_size`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py#L247-L255) \n\n check_dataset_size(\n minimum_dataset_size\n )\n\nRaise an error if the dataset is too small.\n\n### `read`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py#L257-L272) \n\n read()\n\nReturns a large chunk of the Numpy arrays for later re-chunking.\n\n### `read_full`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py#L274-L286) \n\n read_full()\n\nReturns `Tensor` versions of the full Numpy arrays."]]
View source on GitHub