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!10164 add LogUniformCandidateSampler input check 

From: @yanzhenxiang2020
Reviewed-by: @wuxuejian,@liangchenghui
Signed-off-by: @wuxuejian
This commit is contained in:
mindspore-ci-bot 2020-12-19 16:02:39 +08:00 committed by Gitee
parent 735820261a 4252ce0ad3
commit 956e0cf1dc
2 changed files with 6 additions and 4 deletions
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2
mindspore/ops/operations/nn_ops.py
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@ -3423,7 +3423,7 @@ class ComputeAccidentalHits(PrimitiveWithCheck):
- **true_classes** (Tensor) - The target classes. With data type of int32 or int64
and shape [batch_size, num_true].
- **sampled_candidates** (Tensor) - The sampled_candidates output of CandidateSampler,
with shape [num_sampled] and the same type as true_classes.
with data type of int32 or int64 and shape [num_sampled].
Outputs:
Tuple of 3 Tensors.

8
mindspore/ops/operations/random_ops.py
View File

@ -602,9 +602,10 @@ class LogUniformCandidateSampler(PrimitiveWithInfer):
Args:
num_true (int): The number of target classes per training example. Default: 1.
num_sampled (int): The number of classes to randomly sample. Default: 5.
unique (bool): Determines whether sample with rejection. If unique is True,
all sampled classes in a batch are unique. Default: True.
range_max (int): The number of possible classes. Default: 5.
unique (bool): Determines whether sample with rejection. If `unique` is True,
all sampled classes in a batch are unique. Default: True.
range_max (int): The number of possible classes. When `unique` is True,
`range_max` must be greater than or equal to `num_sampled`. Default: 5.
seed (int): Random seed, must be non-negative.
Inputs:
@ -644,6 +645,7 @@ class LogUniformCandidateSampler(PrimitiveWithInfer):
Validator.check_value_type("seed", seed, [int], self.name)
self.num_true = Validator.check_number("num_true", num_true, 1, Rel.GE, self.name)
self.num_sampled = Validator.check_number("num_sampled", num_sampled, 1, Rel.GE, self.name)
Validator.check_number("range_max", range_max, 1, Rel.GE, self.name)
if unique:
Validator.check("range_max", range_max, "num_sampled", num_sampled, Rel.GE, self.name)
self.range_max = range_max