isclose

paddle. isclose ( x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None ) [source]

Check if all \(x\) and \(y\) satisfy the condition:

\[\left| x - y \right| \leq atol + rtol \times \left| y \right|\]

elementwise, for all elements of \(x\) and \(y\). The behaviour of this operator is analogous to \(numpy.isclose\), namely that it returns \(True\) if two tensors are elementwise equal within a tolerance.

Parameters

  • x (Tensor) – The input tensor, it’s data type should be float16, float32, float64, complex64, complex128.

  • y (Tensor) – The input tensor, it’s data type should be float16, float32, float64, complex64, complex128.

  • rtol (rtoltype, optional) – The relative tolerance. Default: \(1e-5\) .

  • atol (atoltype, optional) – The absolute tolerance. Default: \(1e-8\) .

  • equal_nan (equalnantype, optional) – If \(True\) , then two \(NaNs\) will be compared as equal. Default: \(False\) .

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

Returns

The output tensor, it’s data type is bool.

Return type

Tensor

Examples

>>> import paddle

>>> x = paddle.to_tensor([10000., 1e-07])
>>> y = paddle.to_tensor([10000.1, 1e-08])
>>> result1 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
...                          equal_nan=False, name="ignore_nan")
>>> print(result1)
Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True,
[True , False])
>>> result2 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
...                          equal_nan=True, name="equal_nan")
>>> print(result2)
Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True,
[True , False])
>>> x = paddle.to_tensor([1.0, float('nan')])
>>> y = paddle.to_tensor([1.0, float('nan')])
>>> result1 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
...                          equal_nan=False, name="ignore_nan")
>>> print(result1)
Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True,
[True , False])
>>> result2 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
...                          equal_nan=True, name="equal_nan")
>>> print(result2)
Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True,
[True, True])