HardSigmoid#

HardSigmoid - 6 #

Version

name: HardSigmoid (GitHub)
domain: main
since_version: 6
function: True
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 6.

Summary

HardSigmoid takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)), is applied to the tensor elementwise.

Attributes

alpha: Value of alpha. Default value is 0.20000000298023224.
beta: Value of beta. Default value is 0.5.

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.

Examples

default

node = onnx.helper.make_node(
    "HardSigmoid", inputs=["x"], outputs=["y"], alpha=0.5, beta=0.6
)

x = np.array([-1, 0, 1]).astype(np.float32)
y = np.clip(x * 0.5 + 0.6, 0, 1)  # expected output [0.1, 0.6, 1.]
expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_example")

x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x * 0.5 + 0.6, 0, 1)
expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid")

_hardsigmoid_default

default_alpha = 0.2
default_beta = 0.5
node = onnx.helper.make_node(
    "HardSigmoid",
    inputs=["x"],
    outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x * default_alpha + default_beta, 0, 1)
expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_default")

Differences

 HardSigmoid takes one input data (Tensor) and produces one output data
 (Tensor) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)),
 is applied to the tensor elementwise.
 **Attributes**
 * **alpha**:
-  Value of alpha default to 0.2 Default value is 0.20000000298023224.
+  Value of alpha. Default value is 0.20000000298023224.
 * **beta**:
-  Value of beta default to 0.5 Default value is 0.5.
+  Value of beta. Default value is 0.5.
-* **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.

HardSigmoid - 1 #