/TensorFlow 2.4


Creates a dataset that takes a Bernoulli sample of the contents of another dataset.

There is no transformation in the tf.data Python API for creating this dataset. Instead, it is created as a result of the filter_with_random_uniform_fusion static optimization. Whether this optimization is performed is determined by the experimental_optimization.filter_with_random_uniform_fusion option of tf.data.Options.

input_dataset A Tensor of type variant.
rate A Tensor of type float32. A scalar representing the sample rate. Each element of input_dataset is retained with this probability, independent of all other elements.
seed A Tensor of type int64. A scalar representing seed of random number generator.
seed2 A Tensor of type int64. A scalar representing seed2 of random number generator.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).
A Tensor of type variant.

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Code samples licensed under the Apache 2.0 License.