Softplus#

Softplus - 1 #

Version

name: Softplus (GitHub)
domain: main
since_version: 1
function: True
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 1.

Summary

Softplus takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the softplus function, y = ln(exp(x) + 1), is applied to the tensor elementwise.

Inputs

X (heterogeneous) - T: 1D input tensor

Outputs

Y (heterogeneous) - T: 1D input tensor

Type Constraints

T in ( 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(
    "Softplus",
    inputs=["x"],
    outputs=["y"],
)

x = np.array([-1, 0, 1]).astype(np.float32)
y = np.log(
    np.exp(x) + 1
)  # expected output [0.31326166, 0.69314718, 1.31326163]
expect(node, inputs=[x], outputs=[y], name="test_softplus_example")

x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.log(np.exp(x) + 1)
expect(node, inputs=[x], outputs=[y], name="test_softplus")

Softplus#

Softplus - 1#

Softplus - 1 #