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.
|