torch.clamp(input,min,max)
- 对输入的 input 张量中每个值做截断操作
截断方式如下
y i = { m i n if x i <min x i if min <= x i <= max m a x if x i > max y_i= \left\{\begin{array}{ll} min & \textrm{if $x_i$<min}\\ x_i & \textrm{if min <= $x_i$ <= max}\\ max & \textrm{if $x_i$ > max} \end{array}\right. yi=⎩⎨⎧minximaxif xi<minif min <= xi <= maxif xi > max
input(Tensor)，输入张量；min(Number)，截断范围最小值；max(Number)，截断范围最大值；
例子如下
>>> a = torch.randn(4)
>>> a
tensor([-1.7120, 0.1734, -0.0478, -0.0922])
>>> torch.clamp(a, min=-0.5, max=0.5)
tensor([-0.5000, 0.1734, -0.0478, -0.0922])
torch.div(input,other)
- 将
input张量中值除以other张量中对应的元素值，other可为张量也可为 标量，转换方式如下
o u t i = i n p u t i o t h e r i out_i = \frac{input_i}{other_i} outi=otheriinputi
参数解析
input(Tensor)，输入张量，作为被除数；other(Tensor or Number)，作除数的张量或标量；
代码实例：
>>> a = torch.randn(5)
>>> a
tensor([ 0.3810, 1.2774, -0.2972, -0.3719, 0.4637])
>>> torch.div(a, 0.5)
tensor([ 0.7620, 2.5548, -0.5944, -0.7439, 0.9275])
>>> a = torch.randn(4, 4)
>>> a
tensor([[-0.3711, -1.9353, -0.4605, -0.2917],
[ 0.1815, -1.0111, 0.9805, -1.5923],
[ 0.1062, 1.4581, 0.7759, -1.2344],
[-0.1830, -0.0313, 1.1908, -1.4757]])
>>> b = torch.randn(4)
>>> b
tensor([ 0.8032, 0.2930, -0.8113, -0.2308])
>>> torch.div(a, b)
tensor([[-0.4620, -6.6051, 0.5676, 1.2637],
[ 0.2260, -3.4507, -1.2086, 6.8988],
[ 0.1322, 4.9764, -0.9564, 5.3480],
[-0.2278, -0.1068, -1.4678, 6.3936]])