fmin

paddle. fmin ( x, y, name=None ) [source]

Compares the elements at the corresponding positions of the two tensors and returns a new tensor containing the minimum value of the element. If one of them is a nan value, the other value is directly returned, if both are nan values, then the first nan value is returned. The equation is:

\[out = fmin(x, y)\]

Note: paddle.fmin supports broadcasting. If you want know more about broadcasting, please refer to user_guide_broadcasting .

Parameters
  • x (Tensor) – the input tensor, it’s data type should be float32, float64, int32, int64.

  • y (Tensor) – the input tensor, it’s data type should be float32, float64, int32, int64.

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

Returns

N-D Tensor. A location into which the result is stored. If x, y have different shapes and are “broadcastable”, the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y.

Examples

import numpy as np
import paddle

x = paddle.to_tensor([[1, 2], [7, 8]])
y = paddle.to_tensor([[3, 4], [5, 6]])
res = paddle.fmin(x, y)
print(res)
#       [[1, 2],
#        [5, 6]]

x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]])
y = paddle.to_tensor([3, 0, 4])
res = paddle.fmin(x, y)
print(res)
#       [[[1, 0, 3],
#         [1, 0, 3]]]

x = paddle.to_tensor([2, 3, 5], dtype='float32')
y = paddle.to_tensor([1, np.nan, np.nan], dtype='float32')
res = paddle.fmin(x, y)
print(res)
#       [ 1., 3., 5.]

x = paddle.to_tensor([5, 3, np.inf], dtype='float64')
y = paddle.to_tensor([1, -np.inf, 5], dtype='float64')
res = paddle.fmin(x, y)
print(res)
#       [   1., -inf.,    5.]