# isclose¶

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

This operator checks 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 float32, float64.

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

• 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

Raises
• TypeError – The data type of x must be one of float32, float64.

• TypeError – The data type of y must be one of float32, float64.

• TypeError – The type of rtol must be float.

• TypeError – The type of atol must be float.

• TypeError – The type of equal_nan must be bool.

Examples

import paddle

result1 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
equal_nan=False, name="ignore_nan")
np_result1 = result1.numpy()
# [True, False]
result2 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
equal_nan=True, name="equal_nan")
np_result2 = result2.numpy()
# [True, False]