.. _l-onnx-doc-Sqrt: ==== Sqrt ==== .. contents:: :local: .. _l-onnx-op-sqrt-13: Sqrt - 13 ========= **Version** * **name**: `Sqrt (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** Square root takes one input data (Tensor) and produces one output data (Tensor) where the square root is, y = x^0.5, is applied to the tensor elementwise. If x is negative, then it will return NaN. **Inputs** * **X** (heterogeneous) - **T**: Input tensor **Outputs** * **Y** (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** :: node = onnx.helper.make_node( 'Sqrt', inputs=['x'], outputs=['y'], ) x = np.array([1, 4, 9]).astype(np.float32) y = np.sqrt(x) # expected output [1., 2., 3.] expect(node, inputs=[x], outputs=[y], name='test_sqrt_example') x = np.abs(np.random.randn(3, 4, 5).astype(np.float32)) y = np.sqrt(x) expect(node, inputs=[x], outputs=[y], name='test_sqrt') **Differences** .. raw:: html
 `0` `0` `Square root takes one input data (Tensor) and produces one output data` `Square root takes one input data (Tensor) and produces one output data` `1` `1` `(Tensor) where the square root is, y = x^0.5, is applied to` `(Tensor) where the square root is, y = x^0.5, is applied to` `2` `2` `the tensor elementwise. If x is negative, then it will return NaN.` `the tensor elementwise. If x is negative, then it will return NaN.` `3` `3` `4` `4` `**Inputs**` `**Inputs**` `5` `5` `6` `6` `* **X** (heterogeneous) - **T**:` `* **X** (heterogeneous) - **T**:` `7` `7` ` Input tensor` ` Input tensor` `8` `8` `9` `9` `**Outputs**` `**Outputs**` `10` `10` `11` `11` `* **Y** (heterogeneous) - **T**:` `* **Y** (heterogeneous) - **T**:` `12` `12` ` Output tensor` ` Output tensor` `13` `13` `14` `14` `**Type Constraints**` `**Type Constraints**` `15` `15` `16` `16` `* **T** in (` `* **T** in (` `17` ` tensor(bfloat16),` `17` `18` ` tensor(double),` ` tensor(double),` `18` `19` ` tensor(float),` ` tensor(float),` `19` `20` ` tensor(float16)` ` tensor(float16)` `20` `21` ` ):` ` ):` `21` `22` ` Constrain input and output types to float tensors.` ` Constrain input and output types to float tensors.`
.. _l-onnx-op-sqrt-6: Sqrt - 6 ======== **Version** * **name**: `Sqrt (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** Square root takes one input data (Tensor) and produces one output data (Tensor) where the square root is, y = x^0.5, is applied to the tensor elementwise. If x is negative, then it will return NaN. **Inputs** * **X** (heterogeneous) - **T**: Input tensor **Outputs** * **Y** (heterogeneous) - **T**: Output tensor **Type Constraints** * **T** in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors. **Differences** .. raw:: html
 `0` `0` `Square root takes one input data (Tensor) and produces one output data` `Square root takes one input data (Tensor) and produces one output data` `1` `1` `(Tensor) where the square root is, y = x^0.5, is applied to` `(Tensor) where the square root is, y = x^0.5, is applied to` `2` `2` `the tensor elementwise. If x is negative, then it will return NaN.` `the tensor elementwise. If x is negative, then it will return NaN.` `3` `3` `4` `**Attributes**` `5` `6` `* **consumed_inputs**:` `7` ` legacy optimization attribute.` `8` `9` `4` `**Inputs**` `**Inputs**` `10` `5` `11` `6` `* **X** (heterogeneous) - **T**:` `* **X** (heterogeneous) - **T**:` `12` `7` ` Input tensor` ` Input tensor` `13` `8` `14` `9` `**Outputs**` `**Outputs**` `15` `10` `16` `11` `* **Y** (heterogeneous) - **T**:` `* **Y** (heterogeneous) - **T**:` `17` `12` ` Output tensor` ` Output tensor` `18` `13` `19` `14` `**Type Constraints**` `**Type Constraints**` `20` `15` `21` `16` `* **T** in (` `* **T** in (` `22` `17` ` tensor(double),` ` tensor(double),` `23` `18` ` tensor(float),` ` tensor(float),` `24` `19` ` tensor(float16)` ` tensor(float16)` `25` `20` ` ):` ` ):` `26` `21` ` Constrain input and output types to float tensors.` ` Constrain input and output types to float tensors.`
.. _l-onnx-op-sqrt-1: Sqrt - 1 ======== **Version** * **name**: `Sqrt (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** Square root takes one input data (Tensor) and produces one output data (Tensor) where the square root is, y = x^0.5, is applied to the tensor elementwise. If x is negative, then it will return NaN. **Attributes** * **consumed_inputs**: legacy optimization attribute. **Inputs** * **X** (heterogeneous) - **T**: Input tensor **Outputs** * **Y** (heterogeneous) - **T**: Output tensor **Type Constraints** * **T** in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.