Home | History | Annotate | Download | only in base_api 
      1 op {
      2   graph_op_name: "LogUniformCandidateSampler"
      3   in_arg {
      4     name: "true_classes"
      5     description: <<END
      6 A batch_size * num_true matrix, in which each row contains the
      7 IDs of the num_true target_classes in the corresponding original label.
      8 END
      9   }
     10   out_arg {
     11     name: "sampled_candidates"
     12     description: <<END
     13 A vector of length num_sampled, in which each element is
     14 the ID of a sampled candidate.
     15 END
     16   }
     17   out_arg {
     18     name: "true_expected_count"
     19     description: <<END
     20 A batch_size * num_true matrix, representing
     21 the number of times each candidate is expected to occur in a batch
     22 of sampled candidates. If unique=true, then this is a probability.
     23 END
     24   }
     25   out_arg {
     26     name: "sampled_expected_count"
     27     description: <<END
     28 A vector of length num_sampled, for each sampled
     29 candidate representing the number of times the candidate is expected
     30 to occur in a batch of sampled candidates.  If unique=true, then this is a
     31 probability.
     32 END
     33   }
     34   attr {
     35     name: "num_true"
     36     description: <<END
     37 Number of true labels per context.
     38 END
     39   }
     40   attr {
     41     name: "num_sampled"
     42     description: <<END
     43 Number of candidates to randomly sample.
     44 END
     45   }
     46   attr {
     47     name: "unique"
     48     description: <<END
     49 If unique is true, we sample with rejection, so that all sampled
     50 candidates in a batch are unique. This requires some approximation to
     51 estimate the post-rejection sampling probabilities.
     52 END
     53   }
     54   attr {
     55     name: "range_max"
     56     description: <<END
     57 The sampler will sample integers from the interval [0, range_max).
     58 END
     59   }
     60   attr {
     61     name: "seed"
     62     description: <<END
     63 If either seed or seed2 are set to be non-zero, the random number
     64 generator is seeded by the given seed.  Otherwise, it is seeded by a
     65 random seed.
     66 END
     67   }
     68   attr {
     69     name: "seed2"
     70     description: <<END
     71 An second seed to avoid seed collision.
     72 END
     73   }
     74   summary: "Generates labels for candidate sampling with a log-uniform distribution."
     75   description: <<END
     76 See explanations of candidate sampling and the data formats at
     77 go/candidate-sampling.
     78 
     79 For each batch, this op picks a single set of sampled candidate labels.
     80 
     81 The advantages of sampling candidates per-batch are simplicity and the
     82 possibility of efficient dense matrix multiplication. The disadvantage is that
     83 the sampled candidates must be chosen independently of the context and of the
     84 true labels.
     85 END
     86 }
     87