Bernoulli¶

Bernoulli - 22¶

Version¶

name: Bernoulli (GitHub)
domain: main
since_version: 22
function: True
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 22.

Summary¶

Draws binary random numbers (0 or 1) from a Bernoulli distribution. The input tensor should be a tensor containing probabilities p (a value in the range [0,1]) to be used for drawing the binary random number, where an output of 1 is produced with probability p and an output of 0 is produced with probability (1-p).

This operator is non-deterministic and may not produce the same values in different implementations (even if a seed is specified).

Attributes¶

dtype - INT :

The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.
seed - FLOAT :

(Optional) Seed to the random generator, if not specified we will auto generate one.

Inputs¶

input (heterogeneous) - T1:

All values in input have to be in the range:[0, 1].

Outputs¶

output (heterogeneous) - T2:

The returned output tensor only has values 0 or 1, same shape as input tensor.

Type Constraints¶

T1 in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16) ):

Constrain input types to float tensors.
T2 in ( tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):

Constrain output types to all numeric tensors and bool tensors.

Examples¶

_bernoulli_without_dtype¶

import numpy as np
import onnx

node = onnx.helper.make_node(
    "Bernoulli",
    inputs=["x"],
    outputs=["y"],
)

x = np.random.uniform(0.0, 1.0, 10).astype(float)
y = bernoulli_reference_implementation(x, float)
expect(node, inputs=[x], outputs=[y], name="test_bernoulli")

_bernoulli_with_dtype¶

import numpy as np
import onnx

node = onnx.helper.make_node(
    "Bernoulli",
    inputs=["x"],
    outputs=["y"],
    dtype=onnx.TensorProto.DOUBLE,
)

x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
y = bernoulli_reference_implementation(x, float)
expect(node, inputs=[x], outputs=[y], name="test_bernoulli_double")

_bernoulli_with_seed¶

import numpy as np
import onnx

seed = float(0)
node = onnx.helper.make_node(
    "Bernoulli",
    inputs=["x"],
    outputs=["y"],
    seed=seed,
)

x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
y = bernoulli_reference_implementation(x, np.float32)
expect(node, inputs=[x], outputs=[y], name="test_bernoulli_seed")

Bernoulli - 15 vs 22

Bernoulli - 15¶