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

import numpy as np
import onnx

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")

result = np.divide(np.add(data_0, data_1), 2.0)
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"
)

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.

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.

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.