pr to master #8
|
@ -36,14 +36,14 @@ class ReduceLogSumExp(Cell):
|
|||
The dtype of the tensor to be reduced is number.
|
||||
|
||||
Args:
|
||||
keep_dims (bool): If true, keep these reduced dimensions and the length is 1.
|
||||
If false, don't keep these dimensions.
|
||||
Default : False.
|
||||
keep_dims (bool): If True, keep these reduced dimensions and the length is 1.
|
||||
If False, don't keep these dimensions.
|
||||
Default : False.
|
||||
axis (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions.
|
||||
Only constant value is allowed.
|
||||
|
||||
Inputs:
|
||||
- **input_x** (Tensor[Number]) - The input tensor. With float16 or float32 data type.
|
||||
- **axis** (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions.
|
||||
Only constant value is allowed.
|
||||
|
||||
Outputs:
|
||||
Tensor, has the same dtype as the `input_x`.
|
||||
|
@ -57,8 +57,8 @@ class ReduceLogSumExp(Cell):
|
|||
|
||||
Examples:
|
||||
>>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
|
||||
>>> op = nn.ReduceLogSumExp(keep_dims=True)
|
||||
>>> output = op(input_x, 1)
|
||||
>>> op = nn.ReduceLogSumExp(keep_dims=True, 1)
|
||||
>>> output = op(input_x)
|
||||
>>> output.shape
|
||||
(3, 1, 5, 6)
|
||||
"""
|
||||
|
|
Loading…
Reference in New Issue