tf.data.experimental.sample_from_datasets

tf.data.experimental.sample_from_datasets(
    datasets,
    weights=None,
    seed=None
)

Defined in tensorflow/python/data/experimental/ops/interleave_ops.py.

Samples elements at random from the datasets in datasets.

Args:

• datasets: A list of tf.data.Dataset objects with compatible structure.
• weights: (Optional.) A list of len(datasets) floating-point values where weights[i] represents the probability with which an element should be sampled 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.set_random_seed for behavior.

Returns:

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

Raises:

• TypeError: If the datasets or weights arguments have the wrong type.
• ValueError: If the weights argument is specified and does not match the length of the datasets element.