tf.data.experimental.sample_from_datasets

Samples elements at random from the datasets in datasets. (deprecated)

tf.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]

datasets A non-empty list of tf.data.Dataset objects with compatible structure.
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.
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.
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.

A dataset that interleaves elements from datasets at random, according to weights if provided, otherwise with uniform probability.

TypeError If the datasets or weights arguments have the wrong type.
ValueError

  • If datasets is empty, or
  • If weights is specified and does not match the length of datasets.