Creates a dataset that takes a Bernoulli sample of the contents of another dataset.
tf.raw_ops.SamplingDataset(
input_dataset, rate, seed, seed2, output_types, output_shapes, name=None
)
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.
Args | |
|---|---|
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). |
Returns | |
|---|---|
A Tensor of type variant. |