tf.random.uniform_candidate_sampler
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Samples a set of classes using a uniform base distribution.
tf.random.uniform_candidate_sampler(
true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None
)
This operation randomly samples a tensor of sampled classes
(sampled_candidates) from the range of integers [0, range_max).
The elements of sampled_candidates are drawn without replacement
(if unique=True) or with replacement (if unique=False) from
the base distribution.
The base distribution for this operation is the uniform distribution
over the range of integers [0, range_max).
In addition, this operation returns tensors true_expected_count
and sampled_expected_count representing the number of times each
of the target classes (true_classes) and the sampled
classes (sampled_candidates) is expected to occur in an average
tensor of sampled classes. These values correspond to Q(y|x)
defined in this
document.
If unique=True, then these are post-rejection probabilities and we
compute them approximately.
Args |
|---|
true_classes
|
A Tensor of type int64 and shape [batch_size,
num_true]. The target classes.
|
num_true
|
An int. The number of target classes per training example.
|
num_sampled
|
An int. The number of classes to randomly sample. The
sampled_candidates return value will have shape [num_sampled]. If
unique=True, num_sampled must be less than or equal to range_max.
|
unique
|
A bool. Determines whether all sampled classes in a batch are
unique.
|
range_max
|
An int. The number of possible classes.
|
seed
|
An int. An operation-specific seed. Default is 0.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
sampled_candidates
|
A tensor of type int64 and shape [num_sampled]. The
sampled classes, either with possible duplicates (unique=False) or all
unique (unique=True). In either case, sampled_candidates is
independent of the true classes.
|
true_expected_count
|
A tensor of type float. Same shape as
true_classes. The expected counts under the sampling distribution
of each of true_classes.
|
sampled_expected_count
|
A tensor of type float. Same shape as
sampled_candidates. The expected counts under the sampling distribution
of each of sampled_candidates.
|
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Last updated 2021-05-14 UTC.
[null,null,["Last updated 2021-05-14 UTC."],[],[],null,["# tf.random.uniform_candidate_sampler\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/random/uniform_candidate_sampler) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.5.0/tensorflow/python/ops/candidate_sampling_ops.py#L31-L88) |\n\nSamples a set of classes using a uniform base distribution.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.nn.uniform_candidate_sampler`](https://www.tensorflow.org/api_docs/python/tf/random/uniform_candidate_sampler), [`tf.compat.v1.random.uniform_candidate_sampler`](https://www.tensorflow.org/api_docs/python/tf/random/uniform_candidate_sampler)\n\n\u003cbr /\u003e\n\n tf.random.uniform_candidate_sampler(\n true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None\n )\n\nThis operation randomly samples a tensor of sampled classes\n(`sampled_candidates`) from the range of integers `[0, range_max)`.\n\nThe elements of `sampled_candidates` are drawn without replacement\n(if `unique=True`) or with replacement (if `unique=False`) from\nthe base distribution.\n\nThe base distribution for this operation is the uniform distribution\nover the range of integers `[0, range_max)`.\n\nIn addition, this operation returns tensors `true_expected_count`\nand `sampled_expected_count` representing the number of times each\nof the target classes (`true_classes`) and the sampled\nclasses (`sampled_candidates`) is expected to occur in an average\ntensor of sampled classes. These values correspond to `Q(y|x)`\ndefined in [this\ndocument](http://www.tensorflow.org/extras/candidate_sampling.pdf).\nIf `unique=True`, then these are post-rejection probabilities and we\ncompute them approximately.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `true_classes` | A `Tensor` of type `int64` and shape `[batch_size, num_true]`. The target classes. |\n| `num_true` | An `int`. The number of target classes per training example. |\n| `num_sampled` | An `int`. The number of classes to randomly sample. The `sampled_candidates` return value will have shape `[num_sampled]`. If `unique=True`, `num_sampled` must be less than or equal to `range_max`. |\n| `unique` | A `bool`. Determines whether all sampled classes in a batch are unique. |\n| `range_max` | An `int`. The number of possible classes. |\n| `seed` | An `int`. An operation-specific seed. Default is 0. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|--------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `sampled_candidates` | A tensor of type `int64` and shape `[num_sampled]`. The sampled classes, either with possible duplicates (`unique=False`) or all unique (`unique=True`). In either case, `sampled_candidates` is independent of the true classes. |\n| `true_expected_count` | A tensor of type `float`. Same shape as `true_classes`. The expected counts under the sampling distribution of each of `true_classes`. |\n| `sampled_expected_count` | A tensor of type `float`. Same shape as `sampled_candidates`. The expected counts under the sampling distribution of each of `sampled_candidates`. |\n\n\u003cbr /\u003e"]]
TensorFlow 1 version
View source on GitHub