.. _l-onnx-doc-Sigmoid: ======= Sigmoid ======= .. contents:: :local: .. _l-onnx-op-sigmoid-13: Sigmoid - 13 ============ **Version** * **name**: `Sigmoid (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** Sigmoid takes one input data (Tensor) and produces one output data (Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the tensor elementwise. **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( "Sigmoid", inputs=["x"], outputs=["y"], ) x = np.array([-1, 0, 1]).astype(np.float32) y = 1.0 / ( 1.0 + np.exp(np.negative(x)) ) # expected output [0.26894143, 0.5, 0.7310586] expect(node, inputs=[x], outputs=[y], name="test_sigmoid_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = 1.0 / (1.0 + np.exp(np.negative(x))) expect(node, inputs=[x], outputs=[y], name="test_sigmoid") **Differences** .. raw:: html
00Sigmoid takes one input data (Tensor) and produces one output dataSigmoid takes one input data (Tensor) and produces one output data
11(Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the(Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the
22tensor elementwise.tensor elementwise.
33
44**Inputs****Inputs**
55
66* **X** (heterogeneous) - **T**:* **X** (heterogeneous) - **T**:
77 Input tensor Input tensor
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99**Outputs****Outputs**
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1111* **Y** (heterogeneous) - **T**:* **Y** (heterogeneous) - **T**:
1212 Output tensor Output tensor
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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-sigmoid-6: Sigmoid - 6 =========== **Version** * **name**: `Sigmoid (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** Sigmoid takes one input data (Tensor) and produces one output data (Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the tensor elementwise. **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
00Sigmoid takes one input data (Tensor) and produces one output dataSigmoid takes one input data (Tensor) and produces one output data
11(Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the(Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the
22tensor elementwise.tensor elementwise.
33
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
138
149**Outputs****Outputs**
1510
1611* **Y** (heterogeneous) - **T**:* **Y** (heterogeneous) - **T**:
1712 Output tensor Output tensor
1813
1914**Type Constraints****Type Constraints**
2015
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-sigmoid-1: Sigmoid - 1 =========== **Version** * **name**: `Sigmoid (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** Sigmoid takes one input data (Tensor) and produces one output data (Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the tensor elementwise. **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.