paddle. var ( x, axis=None, unbiased=True, keepdim=False, name=None ) [source]

Computes the variance of x along axis .

  • x (Tensor) – The input Tensor with data type float16, float32, float64.

  • axis (int|list|tuple, optional) –

    The axis along which to perform variance calculations. axis should be int, list(int) or tuple(int).

    • If axis is a list/tuple of dimension(s), variance is calculated along all element(s) of axis . axis or element(s) of axis should be in range [-D, D), where D is the dimensions of x .

    • If axis or element(s) of axis is less than 0, it works the same way as \(axis + D\) .

    • If axis is None, variance is calculated over all elements of x. Default is None.

  • unbiased (bool, optional) – Whether to use the unbiased estimation. If unbiased is True, the divisor used in the computation is \(N - 1\), where \(N\) represents the number of elements along axis , otherwise the divisor is \(N\). Default is True.

  • keep_dim (bool, optional) – Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the input unless keep_dim is true. Default is False.

  • name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.


Tensor, results of variance along axis of x, with the same data type as x.


>>> import paddle

>>> x = paddle.to_tensor([[1.0, 2.0, 3.0], [1.0, 4.0, 5.0]])
>>> out1 = paddle.var(x)
>>> print(out1.numpy())
>>> out2 = paddle.var(x, axis=1)
>>> print(out2.numpy())
[1.         4.3333335]