clamp

paddle. clamp ( x: Tensor, min: float | None = None, max: float | None = None, name: str | None = None, *, out: Tensor | None = None ) → Tensor

This operator clip all elements in input into the range [ min, max ] and return a resulting tensor as the following equation:

\[Out = MIN(MAX(x, min), max)\]

Note

Alias Support: The parameter name input can be used as an alias for x. For example, clip(input=tensor_x) is equivalent to clip(x=tensor_x).

Parameters

x (Tensor) – An N-D Tensor with data type bfloat16, float16, float32, float64, int32 or int64. alias: input.
min (float|int|Tensor, optional) – The lower bound with type float , int or a 0-D Tensor with shape [] and type bfloat16, float16, float32, float64, int32.
max (float|int|Tensor, optional) – The upper bound with type float, int or a 0-D Tensor with shape [] and type bfloat16, float16, float32, float64, int32.
name (str|None, optional) – Name for the operation (optional, default is None). For more information, please refer to api_guide_Name.
out (Tensor, optional) – The output Tensor. If set, the result will be stored in this Tensor. Default: None.

Returns

A Tensor with the same data shape as input. If either min or max is a floating-point value/Tensor, the output tensor will have a data type of float32. Otherwise, the output tensor will inherit the same data type as the input.

Return type

Tensor

Examples

           >>> import paddle

>>> x1 = paddle.to_tensor([[1.2, 3.5], [4.5, 6.4]], 'float32')
>>> out1 = paddle.clip(x1, min=3.5, max=5.0)
>>> out1
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[3.50000000, 3.50000000],
 [4.50000000, 5.        ]])
>>> out2 = paddle.clip(x1, min=2.5)
>>> out2
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[2.50000000, 3.50000000],
 [4.50000000, 6.40000010]])

          