Elu#

Elu - 6 #

Version

name: Elu (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

Elu takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the function f(x) = alpha * (exp(x) - 1.) for x < 0, f(x) = x for x >= 0., is applied to the tensor elementwise.

Attributes

alpha: Coefficient of ELU.

Inputs

X (heterogeneous) - T: 1D input tensor

Outputs

Y (heterogeneous) - T: 1D output 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("Elu", inputs=["x"], outputs=["y"], alpha=2.0)

x = np.array([-1, 0, 1]).astype(np.float32)
# expected output [-1.2642411, 0., 1.]
y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
expect(node, inputs=[x], outputs=[y], name="test_elu_example")

x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
expect(node, inputs=[x], outputs=[y], name="test_elu")

_elu_default

import numpy as np
import onnx

default_alpha = 1.0
node = onnx.helper.make_node(
    "Elu",
    inputs=["x"],
    outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha
expect(node, inputs=[x], outputs=[y], name="test_elu_default")

Elu - 1 vs 6

Elu - 1 #