(l-onnx-doc-ReduceMin)=

# ReduceMin


(l-onnx-op-reducemin-20)=

## ReduceMin - 20

### Version

- **name**: [ReduceMin (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#ReduceMin)
- **domain**: `main`
- **since_version**: `20`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 20**.

### Summary

Computes the min of the input tensor's elements along the provided axes. The resulting
tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then
the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are
valid. Reduction over an empty set of values yields plus infinity (if supported by the datatype) or the maximum value of the data type otherwise.

If the input data type is Boolean, the comparison should consider `False < True`.

The above behavior is similar to numpy, with the exception that numpy defaults `keepdims`
to `False` instead of `True`.

### Attributes

* **keepdims - INT** (default is `'1'`):

  Keep the reduced dimension or not, default 1 means keep reduced dimension.

* **noop_with_empty_axes - INT** (default is `'0'`):

  Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.

### Inputs

Between 1 and 2 inputs.

- **data** (heterogeneous) - **T**:

  An input tensor.
- **axes** (optional, heterogeneous) - **tensor(int64)**:

  Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).

### Outputs

- **reduced** (heterogeneous) - **T**:

  Reduced output tensor.

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(bool)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain input and output types to numeric and Boolean tensors.

```{toctree}
text_diff_ReduceMin_18_20
```

(l-onnx-op-reducemin-18)=

## ReduceMin - 18

### Version

- **name**: [ReduceMin (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#ReduceMin)
- **domain**: `main`
- **since_version**: `18`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 18**.

### Summary

Computes the min of the input tensor's elements along the provided axes. The resulting
tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then
the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are
valid. Reduction over an empty set of values yields plus infinity (if supported by the datatype) or the maximum value of the data type otherwise.

The above behavior is similar to numpy, with the exception that numpy defaults `keepdims`
to `False` instead of `True`.

### Attributes

* **keepdims - INT** (default is `'1'`):

  Keep the reduced dimension or not, default 1 means keep reduced dimension.

* **noop_with_empty_axes - INT** (default is `'0'`):

  Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.

### Inputs

Between 1 and 2 inputs.

- **data** (heterogeneous) - **T**:

  An input tensor.
- **axes** (optional, heterogeneous) - **tensor(int64)**:

  Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).

### Outputs

- **reduced** (heterogeneous) - **T**:

  Reduced output tensor.

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain input and output types to numeric tensors.

```{toctree}
text_diff_ReduceMin_13_20
text_diff_ReduceMin_13_18
```

(l-onnx-op-reducemin-13)=

## ReduceMin - 13

### Version

- **name**: [ReduceMin (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#ReduceMin)
- **domain**: `main`
- **since_version**: `13`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 13**.

### Summary

Computes the min of the input tensor's elements along the provided axes. The resulting
tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then
the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are
valid. Reduction over an empty set of values yields plus infinity (if supported by the datatype) or the maximum value of the data type otherwise.

The above behavior is similar to numpy, with the exception that numpy defaults `keepdims`
to `False` instead of `True`.

### Attributes

* **axes - INTS** :

  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).

* **keepdims - INT** (default is `'1'`):

  Keep the reduced dimension or not, default 1 means keep reduced dimension.

### Inputs

- **data** (heterogeneous) - **T**:

  An input tensor.

### Outputs

- **reduced** (heterogeneous) - **T**:

  Reduced output tensor.

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain input and output types to numeric tensors.

```{toctree}
text_diff_ReduceMin_12_20
text_diff_ReduceMin_12_18
text_diff_ReduceMin_12_13
```

(l-onnx-op-reducemin-12)=

## ReduceMin - 12

### Version

- **name**: [ReduceMin (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#ReduceMin)
- **domain**: `main`
- **since_version**: `12`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 12**.

### Summary

Computes the min of the input tensor's element along the provided axes. The resulting
tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
the resulted tensor have the reduced dimension pruned.

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
False instead of True.

### Attributes

* **axes - INTS** :

  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).

* **keepdims - INT** (default is `'1'`):

  Keep the reduced dimension or not, default 1 means keep reduced dimension.

### Inputs

- **data** (heterogeneous) - **T**:

  An input tensor.

### Outputs

- **reduced** (heterogeneous) - **T**:

  Reduced output tensor.

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain input and output types to high-precision and 8 bit numeric tensors.

```{toctree}
text_diff_ReduceMin_11_20
text_diff_ReduceMin_11_18
text_diff_ReduceMin_11_13
text_diff_ReduceMin_11_12
```

(l-onnx-op-reducemin-11)=

## ReduceMin - 11

### Version

- **name**: [ReduceMin (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#ReduceMin)
- **domain**: `main`
- **since_version**: `11`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 11**.

### Summary

Computes the min of the input tensor's element along the provided axes. The resulting
tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are
valid. Reduction over an empty set of values yields plus infinity (if supported by the datatype) or the maximum value of the data type otherwise.

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
False instead of True.

### Attributes

* **axes - INTS** :

  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).

* **keepdims - INT** (default is `'1'`):

  Keep the reduced dimension or not, default 1 means keep reduced dimension.

### Inputs

- **data** (heterogeneous) - **T**:

  An input tensor.

### Outputs

- **reduced** (heterogeneous) - **T**:

  Reduced output tensor.

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`, `tensor(int64)`, `tensor(uint32)`, `tensor(uint64)` ):

  Constrain input and output types to high-precision numeric tensors.

```{toctree}
text_diff_ReduceMin_1_20
text_diff_ReduceMin_1_18
text_diff_ReduceMin_1_13
text_diff_ReduceMin_1_12
text_diff_ReduceMin_1_11
```

(l-onnx-op-reducemin-1)=

## ReduceMin - 1

### Version

- **name**: [ReduceMin (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#ReduceMin)
- **domain**: `main`
- **since_version**: `1`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 1**.

### Summary

Computes the min of the input tensor's element along the provided axes. The resulting
tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are
valid. Reduction over an empty set of values yields plus infinity (if supported by the datatype) or the maximum value of the data type otherwise.

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
False instead of True.

### Attributes

* **axes - INTS** :

  A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.

* **keepdims - INT** (default is `'1'`):

  Keep the reduced dimension or not, default 1 means keep reduced dimension.

### Inputs

- **data** (heterogeneous) - **T**:

  An input tensor.

### Outputs

- **reduced** (heterogeneous) - **T**:

  Reduced output tensor.

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`, `tensor(int64)`, `tensor(uint32)`, `tensor(uint64)` ):

  Constrain input and output types to high-precision numeric tensors.