- paddle. nansum ( x, axis=None, dtype=None, keepdim=False, name=None )
Computes the sum of tensor elements over the given axis, treating Not a Numbers (NaNs) as zero.
x (Tensor) – An N-D Tensor, the data type is float16, float32, float64, int32 or int64.
axis (int|list|tuple, optional) – The dimensions along which the nansum is performed. If
None, nansum all elements of
xand return a Tensor with a single element, otherwise must be in the range \([-rank(x), rank(x))\). If \(axis[i] < 0\), the dimension to reduce is \(rank + axis[i]\).
dtype (str, optional) – The dtype of output Tensor. The default value is None, the dtype of output is the same as input Tensor x.
keepdim (bool, optional) – Whether to reserve the reduced dimension in the output Tensor. The result Tensor will have one fewer dimension than the
keepdimis true, default value is False.
name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.
Results of summation operation on the specified axis of input Tensor x,
- Return type
import paddle # x is a Tensor with following elements: # [[nan, 0.3, 0.5, 0.9] # [0.1, 0.2, -nan, 0.7]] # Each example is followed by the corresponding output tensor. x = paddle.to_tensor([[float('nan'), 0.3, 0.5, 0.9], [0.1, 0.2, float('-nan'), 0.7]],dtype="float32") out1 = paddle.nansum(x) # 2.7 out2 = paddle.nansum(x, axis=0) # [0.1, 0.5, 0.5, 1.6] out3 = paddle.nansum(x, axis=-1) # [1.7, 1.0] out4 = paddle.nansum(x, axis=1, keepdim=True) # [[1.7], [1.0]] # y is a Tensor with shape [2, 2, 2] and elements as below: # [[[1, nan], [3, 4]], # [[5, 6], [-nan, 8]]] # Each example is followed by the corresponding output tensor. y = paddle.to_tensor([[[1, float('nan')], [3, 4]], [[5, 6], [float('-nan'), 8]]]) out5 = paddle.nansum(y, axis=[1, 2]) # [8, 19] out6 = paddle.nansum(y, axis=[0, 1]) # [9, 18]