Sigmoid¶
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<T>) and produces one output data (Tensor<T>) 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
import numpy as np
import onnx
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")
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<T>) and produces one output data (Tensor<T>) 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.
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<T>) and produces one output data (Tensor<T>) 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.