Defined in tensorflow/python/data/experimental/ops/

Samples elements at random from the datasets in datasets.


  • datasets: A list of 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 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.


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 the weights argument is specified and does not match the length of the datasets element.