# tf.random.uniform_candidate_sampler

Samples a set of classes using a uniform base distribution.

This operation randomly samples a tensor of sampled classes (`sampled_candidates`) from the range of integers `[0, range_max)`.

See the Candidate Sampling Algorithms Reference for a quick course on Candidate Sampling.

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 the Candidate Sampling Algorithms Reference. If `unique=True`, then these are post-rejection probabilities and we compute them approximately.

Note that this function (and also other `*_candidate_sampler` functions) only gives you the ingredients to implement the various Candidate Sampling algorithms listed in the big table in the Candidate Sampling Algorithms Reference. You still need to implement the algorithms yourself.

For example, according to that table, the phrase "negative samples" may mean different things in different algorithms. For instance, in NCE, "negative samples" means `S_i` (which is just the sampled classes) which may overlap with true classes, while in Sampled Logistic, "negative samples" means `S_i - T_i` which excludes the true classes. The return value `sampled_candidates` corresponds to `S_i`, not to any specific definition of "negative samples" in any specific algorithm. It's your responsibility to pick an algorithm and calculate the "negative samples" defined by that algorithm (e.g. `S_i - T_i`).

As another example, the `true_classes` argument is for calculating the `true_expected_count` output (as a by-product of this function's main calculation), which may be needed by some algorithms (according to that table). It's not for excluding true classes in the return value `sampled_candidates`. Again that step is algorithm-specific and should be carried out by you.

`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).

`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`). As noted above, `sampled_candidates` may overlap with 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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{"lastModified": "Last updated 2024-01-23 UTC."}