.. _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
00Square root takes one input data (Tensor) and produces one output dataSquare root takes one input data (Tensor) and produces one output data
11(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
22the tensor elementwise. If x is negative, then it will return NaN.the tensor elementwise. If x is negative, then it will return NaN.
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44**Inputs****Inputs**
55
66* **X** (heterogeneous) - **T**:* **X** (heterogeneous) - **T**:
77 Input tensor Input tensor
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99**Outputs****Outputs**
1010
1111* **Y** (heterogeneous) - **T**:* **Y** (heterogeneous) - **T**:
1212 Output tensor Output tensor
1313
1414**Type Constraints****Type Constraints**
1515
1616* **T** in (* **T** in (
17 tensor(bfloat16),
1718 tensor(double), tensor(double),
1819 tensor(float), tensor(float),
1920 tensor(float16) tensor(float16)
2021 ): ):
2122 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
00Square root takes one input data (Tensor) and produces one output dataSquare root takes one input data (Tensor) and produces one output data
11(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
22the tensor elementwise. If x is negative, then it will return NaN.the tensor elementwise. If x is negative, then it will return NaN.
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4**Attributes**
5
6* **consumed_inputs**:
7 legacy optimization attribute.
8
94**Inputs****Inputs**
105
116* **X** (heterogeneous) - **T**:* **X** (heterogeneous) - **T**:
127 Input tensor Input tensor
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149**Outputs****Outputs**
1510
1611* **Y** (heterogeneous) - **T**:* **Y** (heterogeneous) - **T**:
1712 Output tensor Output tensor
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1914**Type Constraints****Type Constraints**
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2116* **T** in (* **T** in (
2217 tensor(double), tensor(double),
2318 tensor(float), tensor(float),
2419 tensor(float16) tensor(float16)
2520 ): ):
2621 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.