ReduceMax - 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Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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.
- ReduceMax1 → ReduceMax11 +1 -1
ReduceMax1 → ReduceMax11
RENAMED
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@@ -1 +1 @@
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Computes the max of the input tensor's element along the provided axes. The resulting
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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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the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are
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valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or the minimum value of the data type otherwise.
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The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
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False instead of True.
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### Attributes
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* **axes - INTS** :
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-
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
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+
A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. 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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### 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) - **T**:
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Reduced output tensor.
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### Type Constraints
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* **T** in ( tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ):
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Constrain input and output types to high-precision numeric tensors.
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