ReduceMin - 1 vs 11#
Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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.
- ReduceMin1 → ReduceMin11 +1 -2
ReduceMin1 → ReduceMin11
RENAMED
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@@ -1 +1 @@
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1
1
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Computes the min of the input tensor's element along the provided axes. The resulting
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2
2
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tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
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3
3
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the resulted tensor have the reduced dimension pruned.
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4
4
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The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
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5
5
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False instead of True.
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6
6
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**Attributes**
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7
7
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* **axes**:
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8
8
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A list of integers, along which to reduce. The default is to reduce
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9
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-
over all the dimensions of the input tensor.
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9
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+
over all the dimensions of the input tensor.
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10
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-
r-1] where r = rank(data).
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11
10
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* **keepdims**:
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12
11
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Keep the reduced dimension or not, default 1 means keep reduced
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13
12
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dimension.
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14
13
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**Inputs**
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15
14
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* **data** (heterogeneous) - **T**:
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16
15
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An input tensor.
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16
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**Outputs**
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18
17
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* **reduced** (heterogeneous) - **T**:
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19
18
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Reduced output tensor.
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20
19
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**Type Constraints**
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* **T** in (
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22
21
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tensor(double),
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23
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tensor(float),
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24
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tensor(float16),
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25
24
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tensor(int32),
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26
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tensor(int64),
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27
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tensor(uint32),
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tensor(uint64)
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29
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):
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30
29
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Constrain input and output types to high-precision numeric tensors.
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