allclose¶

paddle. allclose ( 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 $numpy.allclose$, 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

x = paddle.to_tensor([10000., 1e-07])
y = paddle.to_tensor([10000.1, 1e-08])
result1 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08,
                        equal_nan=False, name="ignore_nan")
np_result1 = result1.numpy()
# [False]
result2 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08,
                            equal_nan=True, name="equal_nan")
np_result2 = result2.numpy()
# [False]

x = paddle.to_tensor([1.0, float('nan')])
y = paddle.to_tensor([1.0, float('nan')])
result1 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08,
                        equal_nan=False, name="ignore_nan")
np_result1 = result1.numpy()
# [False]
result2 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08,
                            equal_nan=True, name="equal_nan")
np_result2 = result2.numpy()
# [True]