ArgMin - 11 vs 13¶
Next section compares an older to a newer version of the same operator after both definition are converted into markdown text. Green means an addition to the newer version, red means a deletion. Anything else is unchanged.
- ArgMin11 → ArgMin13 +9 -3
ArgMin11 → ArgMin13
RENAMED
@@ -1 +1 @@
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Computes the indices of the min elements of the input tensor's element along the
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provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
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If keepdims
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If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
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If select_last_index is True (default False), the index of the last occurrence of the min
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is selected if the min appears more than once in the input. Otherwise the index of the
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-
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first occurrence is selected.
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The type of the output tensor is integer.
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### Attributes
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* **axis - INT** (default is '0'):
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The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
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* **keepdims - INT** (default is '1'):
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Keep the reduced dimension or not, default 1 means keep reduced dimension.
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+
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* **select_last_index - INT** (default is '0'):
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Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).
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### Inputs
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- **data** (heterogeneous) - **T**:
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An input tensor.
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### Outputs
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- **reduced** (heterogeneous) - **tensor(int64)**:
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Reduced output tensor with integer data type.
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### Type Constraints
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* **T** in ( tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):
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* **T** in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):
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Constrain input and output types to all numeric tensors.
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