# Mean#

## Mean - 13#

Version

• name: Mean (GitHub)

• 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

Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

Inputs

Between 1 and 2147483647 inputs.

• data_0 (variadic, heterogeneous) - T: List of tensors for mean.

Outputs

• mean (heterogeneous) - T: Output tensor.

Type Constraints

• T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Examples

default

data_0 = np.array([3, 0, 2]).astype(np.float32)
data_1 = np.array([1, 3, 4]).astype(np.float32)
data_2 = np.array([2, 6, 6]).astype(np.float32)
result = np.array([2, 3, 4]).astype(np.float32)
node = onnx.helper.make_node(
"Mean",
inputs=["data_0", "data_1", "data_2"],
outputs=["result"],
)
expect(
node,
inputs=[data_0, data_1, data_2],
outputs=[result],
name="test_mean_example",
)

node = onnx.helper.make_node(
"Mean",
inputs=["data_0"],
outputs=["result"],
)
expect(node, inputs=[data_0], outputs=[data_0], name="test_mean_one_input")

node = onnx.helper.make_node(
"Mean",
inputs=["data_0", "data_1"],
outputs=["result"],
)
expect(
node, inputs=[data_0, data_1], outputs=[result], name="test_mean_two_inputs"
)

Differences

 0 0 Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). 1 1 All inputs and outputs must have the same data type. All inputs and outputs must have the same data type. 2 2 This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check Broadcasting in ONNX _. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check Broadcasting in ONNX _. 3 3 4 4 **Inputs** **Inputs** 5 5 6 6 Between 1 and 2147483647 inputs. Between 1 and 2147483647 inputs. 7 7 8 8 * **data_0** (variadic, heterogeneous) - **T**: * **data_0** (variadic, heterogeneous) - **T**: 9 9 List of tensors for mean. List of tensors for mean. 10 10 11 11 **Outputs** **Outputs** 12 12 13 13 * **mean** (heterogeneous) - **T**: * **mean** (heterogeneous) - **T**: 14 14 Output tensor. Output tensor. 15 15 16 16 **Type Constraints** **Type Constraints** 17 17 18 18 * **T** in ( * **T** in ( 19 tensor(bfloat16), 19 20 tensor(double), tensor(double), 20 21 tensor(float), tensor(float), 21 22 tensor(float16) tensor(float16) 22 23 ): ): 23 24 Constrain input and output types to float tensors. Constrain input and output types to float tensors.

## Mean - 8#

Version

• name: Mean (GitHub)

• domain: main

• since_version: 8

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 8.

Summary

Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

Inputs

Between 1 and 2147483647 inputs.

• data_0 (variadic, heterogeneous) - T: List of tensors for mean.

Outputs

• mean (heterogeneous) - T: Output tensor.

Type Constraints

• T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Differences

 0 Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). 1 All inputs and outputs must have the same data type. 0 2 Element-wise mean of each of the input tensors. All inputs and outputs must This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check Broadcasting in ONNX _. 1 have the same shape and data type. 2 3 3 4 **Inputs** **Inputs** 4 5 5 6 Between 1 and 2147483647 inputs. Between 1 and 2147483647 inputs. 6 7 7 8 * **data_0** (variadic, heterogeneous) - **T**: * **data_0** (variadic, heterogeneous) - **T**: 8 9 List of tensors for Mean. List of tensors for mean. 9 10 10 11 **Outputs** **Outputs** 11 12 12 13 * **mean** (heterogeneous) - **T**: * **mean** (heterogeneous) - **T**: 13 14 Output tensor. Same dimension as inputs. Output tensor. 14 15 15 16 **Type Constraints** **Type Constraints** 16 17 17 18 * **T** in ( * **T** in ( 18 19 tensor(double), tensor(double), 19 20 tensor(float), tensor(float), 20 21 tensor(float16) tensor(float16) 21 22 ): ): 22 23 Constrain input and output types to float tensors. Constrain input and output types to float tensors.

## Mean - 6#

Version

• name: Mean (GitHub)

• domain: main

• since_version: 6

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 6.

Summary

Element-wise mean of each of the input tensors. All inputs and outputs must have the same shape and data type.

Inputs

Between 1 and 2147483647 inputs.

• data_0 (variadic, heterogeneous) - T: List of tensors for Mean.

Outputs

• mean (heterogeneous) - T: Output tensor. Same dimension as inputs.

Type Constraints

• T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Differences

 0 0 Element-wise mean of each of the input tensors. All inputs and outputs must Element-wise mean of each of the input tensors. All inputs and outputs must 1 1 have the same shape and data type. have the same shape and data type. 2 2 3 **Attributes** 4 5 * **consumed_inputs**: 6 legacy optimization attribute. 7 8 3 **Inputs** **Inputs** 9 4 10 5 Between 1 and 2147483647 inputs. Between 1 and 2147483647 inputs. 11 6 12 7 * **data_0** (variadic, heterogeneous) - **T**: * **data_0** (variadic, heterogeneous) - **T**: 13 8 List of tensors for Mean. List of tensors for Mean. 14 9 15 10 **Outputs** **Outputs** 16 11 17 12 * **mean** (heterogeneous) - **T**: * **mean** (heterogeneous) - **T**: 18 13 Output tensor. Same dimension as inputs. Output tensor. Same dimension as inputs. 19 14 20 15 **Type Constraints** **Type Constraints** 21 16 22 17 * **T** in ( * **T** in ( 23 18 tensor(double), tensor(double), 24 19 tensor(float), tensor(float), 25 20 tensor(float16) tensor(float16) 26 21 ): ): 27 22 Constrain input and output types to float tensors. Constrain input and output types to float tensors.

## Mean - 1#

Version

• name: Mean (GitHub)

• domain: main

• since_version: 1

• function: False

• support_level: SupportType.COMMON

• shape inference: False

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

Summary

Element-wise mean of each of the input tensors. All inputs and outputs must have the same shape and data type.

Attributes

• consumed_inputs: legacy optimization attribute.

Inputs

Between 1 and 2147483647 inputs.

• data_0 (variadic, heterogeneous) - T: List of tensors for Mean.

Outputs

• mean (heterogeneous) - T: Output tensor. Same dimension as inputs.

Type Constraints

• T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.