我正在尝试在PyTorch中构建ML情感模型。
我在数据框架中从CMU-MOSEI数据集获取了情感标签，如下所示：
| 快乐|伤心|愤怒|惊喜|厌恶|恐惧|
| --|--|--|--|--|--|
| 1.33 |0.0| 0.0| 0.0| 0.0| 0.0|
| 2.0 |0.0| 0.0| 0.33| 0.0| 0.0|
| 0.0 |0.0|一点三三|0.33| 2.0版本|0.0|
每种情绪可以在0.0 -> 3.0之间的范围内
问题是：

如何对该数据进行归一化，使其范围为0 -> 1：

1.通过以下方式规范化每列：

from sklearn.preprocessing import minmax_scale

for emo in ['happy', 'sad', 'anger', 'surprise', 'disgust', 'fear']:
    mosei[emo] = minmax_scale(mosei[emo])

字符串
这给予我ie：
1.33,0.0,0.0,0.0,0.0,0.0 -> 0.44,0.0,0.0,0.0,0.0,0.0
2.0,0.0,0.0,0.33,0.0,0.0 -> 0.67,0.0,0.0,0.11,0.0,0.0
0.0,0.0,1.33,0.33,2.0,0.0 -> 0.0,0.0,0.44,0.11,0.67,0.0
但对于最后一个例子sum() > 1

2.规范化每列，在数据加载器中执行softmax()

>>> F.softmax(torch.tensor([0.44,0.0,0.0,0.0,0.0,0.0]), dim=0)
tensor([0.2370, 0.1526, 0.1526, 0.1526, 0.1526, 0.1526])

>>> F.softmax(torch.tensor([0.0,0.0,0.44,0.11,0.67,0.0]), dim=0)
tensor([0.1312, 0.1312, 0.2037, 0.1464, 0.2564, 0.1312])

型

3.按行而不是按列进行归一化

>>> minmax_scale([1.33,0.0,0.0,0.0,0.0,0.0])
array([1., 0., 0., 0., 0., 0.])

>>> minmax_scale([0.0,0.0,1.33,0.33,2.0,0.0])
array([0.   , 0.   , 0.665, 0.165, 1.   , 0.   ])

型
但同样是最后一个例子sum() > 1

• 也许又是softmax

F.softmax(torch.tensor([0.   , 0.   , 0.665, 0.165, 1.   , 0.   ]), dim=0)
tensor([0.1131, 0.1131, 0.2199, 0.1334, 0.3074, 0.1131])

型
或者可能有不同/更好的标准化方法？