# tf.raw_ops.UniformCandidateSampler

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Generates labels for candidate sampling with a uniform distribution.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.raw_ops.UniformCandidateSampler`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/UniformCandidateSampler)

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    tf.raw_ops.UniformCandidateSampler(
        true_classes,
        num_true,
        num_sampled,
        unique,
        range_max,
        seed=0,
        seed2=0,
        name=None
    )

See explanations of candidate sampling and the data formats at
go/candidate-sampling.

For each batch, this op picks a single set of sampled candidate labels.

The advantages of sampling candidates per-batch are simplicity and the
possibility of efficient dense matrix multiplication. The disadvantage is that
the sampled candidates must be chosen independently of the context and of the
true labels.

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| ## Args ||
|---|---|
| `true_classes` | A `Tensor` of type `int64`. A batch_size \* num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label. |
| `num_true` | An `int` that is `>= 1`. Number of true labels per context. |
| `num_sampled` | An `int` that is `>= 1`. Number of candidates to randomly sample. |
| `unique` | A `bool`. If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities. |
| `range_max` | An `int` that is `>= 1`. The sampler will sample integers from the interval \[0, range_max). |
| `seed` | An optional `int`. Defaults to `0`. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. |
| `seed2` | An optional `int`. Defaults to `0`. An second seed to avoid seed collision. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
|---|---|
| A tuple of `Tensor` objects (sampled_candidates, true_expected_count, sampled_expected_count). ||
| `sampled_candidates` | A `Tensor` of type `int64`. |
| `true_expected_count` | A `Tensor` of type `float32`. |
| `sampled_expected_count` | A `Tensor` of type `float32`. |

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