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Modified indices supported dtype of SparseApplyProximalAdagrad.

This commit is contained in:
liuxiao93 2021-01-05 16:46:06 +08:00
parent 61c83bef88
commit c122e4bda7
1 changed files with 2 additions and 3 deletions
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5
mindspore/ops/operations/nn_ops.py
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@ -5187,7 +5187,7 @@ class SparseApplyProximalAdagrad(PrimitiveWithCheck):
- **grad** (Tensor) - A tensor of the same type as `var`, for the gradient.
- **indices** (Tensor) - A tensor of indices in the first dimension of `var` and `accum`.
If there are duplicates in `indices`, the behavior is undefined. Must be one of the
following types: int16, int32, int64, uint16, uint32, uint64.
following types: int32, int64.
Outputs:
Tuple of 2 tensors, the updated parameters.
@ -5253,8 +5253,7 @@ class SparseApplyProximalAdagrad(PrimitiveWithCheck):
validator.check_scalar_or_tensor_types_same({"lr": lr_dtype}, [mstype.float16, mstype.float32], self.name)
validator.check_scalar_or_tensor_types_same({"l1": l1_dtype}, [mstype.float16, mstype.float32], self.name)
validator.check_scalar_or_tensor_types_same({"l2": l2_dtype}, [mstype.float16, mstype.float32], self.name)
valid_dtypes = [mstype.int16, mstype.int32, mstype.int64,
mstype.uint16, mstype.uint32, mstype.uint64]
valid_dtypes = [mstype.int32, mstype.int64]
validator.check_tensor_dtype_valid('indices', indices_dtype, valid_dtypes, self.name)