tf.random.all_candidate_sampler

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Generate the set of all classes.

View aliases

Main aliases

tf.nn.all_candidate_sampler

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.nn.all_candidate_sampler, tf.compat.v1.random.all_candidate_sampler, `tf.compat.v2.nn.all_candidate_sampler`, `tf.compat.v2.random.all_candidate_sampler`

tf.random.all_candidate_sampler(
    true_classes, num_true, num_sampled, unique, seed=None, name=None
)

Deterministically generates and returns the set of all possible classes. For testing purposes. There is no need to use this, since you might as well use full softmax or full logistic regression.

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 possible classes.
unique	A bool. Ignored. unique.
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]. This operation deterministically returns the entire range [0, num_sampled].
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. All returned values are 1.0.
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. All returned values are 1.0.