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Samples elements at random from the datasets in datasets. (deprecated)
tf.compat.v1.data.experimental.sample_from_datasets(
datasets, weights=None, seed=None, stop_on_empty_dataset=False
)
Creates a dataset by interleaving elements of datasets with weight[i]
probability of picking an element from dataset i. Sampling is done without
replacement. For example, suppose we have 2 datasets:
dataset1 = tf.data.Dataset.range(0, 3)
dataset2 = tf.data.Dataset.range(100, 103)
Suppose also that we sample from these 2 datasets with the following weights:
sample_dataset = tf.data.Dataset.sample_from_datasets(
[dataset1, dataset2], weights=[0.5, 0.5])
One possible outcome of elements in sample_dataset is:
print(list(sample_dataset.as_numpy_iterator()))
# [100, 0, 1, 101, 2, 102]
Args | |
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datasets
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A non-empty list of tf.data.Dataset objects with compatible
structure.
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weights
|
(Optional.) A list or Tensor of len(datasets) floating-point
values where weights[i] represents the probability to sample from
datasets[i], or a tf.data.Dataset object where each element is such a
list. Defaults to a uniform distribution across datasets.
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seed
|
(Optional.) A tf.int64 scalar tf.Tensor, representing the random
seed that will be used to create the distribution. See
tf.random.set_seed for behavior.
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stop_on_empty_dataset
|
If True, sampling stops if it encounters an empty
dataset. If False, it skips empty datasets. It is recommended to set it
to True. Otherwise, the distribution of samples starts off as the user
intends, but may change as input datasets become empty. This can be
difficult to detect since the dataset starts off looking correct. Default
to False for backward compatibility.
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Returns | |
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A dataset that interleaves elements from datasets at random, according to
weights if provided, otherwise with uniform probability.
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Raises | |
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TypeError
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If the datasets or weights arguments have the wrong type.
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ValueError
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