(l-onnx-doc-Bernoulli)=

# Bernoulli


(l-onnx-op-bernoulli-22)=

## Bernoulli - 22

### Version

- **name**: [Bernoulli (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Bernoulli)
- **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.

```{toctree}
text_diff_Bernoulli_15_22
```

(l-onnx-op-bernoulli-15)=

## Bernoulli - 15

### Version

- **name**: [Bernoulli (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Bernoulli)
- **domain**: `main`
- **since_version**: `15`
- **function**: `True`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 15**.

### 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(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.