使用神经网络检测输入排列

时间:2018-10-03 06:24:24

标签: neural-network

我的网络接受形状为(?,5,5)的输入张量,并输出形状为(?,5,5)的张量。输入张量表示5个元素的排列(每个元素由10个数据点表征)。输出张量指示通过使用5个一热张量检测到的排列。例如

Input:
[
["E","E","E","E","E","E","E","E","E","E"],
["C","C","C","C","C","C","C","C","C","C"],
["A","A","A","A","A","A","A","A","A","A"],
["D","D","D","D","D","D","D","D","D","D"],
["B","B","B","B","B","B","B","B","B","B"]
]

Output:
[
[0,0,0,0,1],
[0,0,1,0,0],
[1,0,0,0,0],
[0,0,0,1,0],
[0,1,0,0,0]
]

这种类型的作品,但通常我会得到每行或每列有多个作品的输出,这在我的场景中显然是非法的。 作为一种替代设计,我尝试使用长度为5!= 120的单热输出张量,其中每个索引代表唯一的排列。 但是,准确性比我的第一种方法差很多。

任何人都可以对如何最好地设计我的输出层以解决此类问题提供一些见识吗?

0 个答案:

没有答案