Description: Performs the Sigmoid operation on the input tensor.
Formula:
- aclnnSigmoid and aclnnInplaceSigmoid implement the same function in different ways. Select a proper operator based on your requirements.
- aclnnSigmoid: An output tensor object needs to be created to store the computation result.
- aclnnInplaceSigmoid: No output tensor object needs to be created, and the computation result is stored in the memory of the input tensor.
- Each operator has calls. First, aclnnSigmoidGetWorkspaceSize or aclnnInplaceSigmoidGetWorkspaceSize is called to obtain input parameters and compute the required workspace size based on the computation process. Then, aclnnSigmoid or aclnnInplaceSigmoid is called to perform computation.
[object Object]
[object Object]
[object Object]
[object Object]
Parameters:
[object Object]- [object Object]Atlas inference series products[object Object] and [object Object]Atlas training series products[object Object]: The data type of self can be FLOAT, FLOAT16, DOUBLE, INT8, INT16, INT32, INT64, UINT8, BOOL, COMPLEX64, or COMPLEX128. The data type of out can be FLOAT, FLOAT16, DOUBLE, COMPLEX64, or COMPLEX128.
Returns:
aclnnStatus: status code. For details, see .
The first-phase API implements input parameter verification. The following errors may be thrown:
[object Object]
Parameters:
[object Object]- [object Object]Atlas inference series products[object Object] and [object Object]Atlas training series products[object Object]: The data type can be FLOAT, FLOAT16, DOUBLE, COMPLEX64, or COMPLEX128.
Returns:
aclnnStatus: status code. For details, see .
The first-phase API implements input parameter verification. The following errors may be thrown:
[object Object]
- Deterministic compute:
- aclnnSigmoid and aclnnInplaceSigmoid default to a deterministic implementation.
The following example is for reference only. For details, see .
[object Object]