将一键编码的目标值映射到正确的标签名称 [英] Mapping one-hot encoded target values to proper label names

问题描述

我有一个枚举的标签名称列表，并创建了一个字典:

I have a list of label names which I enuemrated and created a dictionary:

my_list = [b'airplane',
 b'automobile',
 b'bird',
 b'cat',
 b'deer',
 b'dog',
 b'frog',
 b'horse',
 b'ship',
 b'truck']

label_dict =dict(enumerate(my_list))


{0: b'airplane',
 1: b'automobile',
 2: b'bird',
 3: b'cat',
 4: b'deer',
 5: b'dog',
 6: b'frog',
 7: b'horse',
 8: b'ship',
 9: b'truck'}

现在，我正在尝试将dict值map/apply清除到目标文件中，该目标文件格式为一键编码.

Now I'm trying to cleaning map/apply the dict value to my target which is in an one-hot-encoded form.

y_test[0]

array([ 0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.])


y_test[0].map(label_dict) should return: 
'cat'

我在玩

(lambda key,value: value for y_test[0] == 1)

但无法提出任何具体建议

but couldn't come up with any concrete

谢谢.

推荐答案

由于我们正在使用one-hot encoded数组，因此argmax可用于为每行从1获取一个索引.因此，使用列表作为输入-

Since we are working with one-hot encoded array, argmax could be used to get the index for one off 1 for each row. Thus, using the list as input -

[my_list[i] for i in y_test.argmax(1)]

或使用np.take具有数组输出-

np.take(my_list,y_test.argmax(1))

要使用dict并将连续键假定为0,1,..，我们可以使用-

To work with dict and assuming sequential keys as 0,1,.., we could have -

np.take(label_dict.values(),y_test.argmax(1))

如果键本质上不是按顺序排列的，而是排序的-

If the keys are not essentially in sequence but sorted -

np.take(label_dict.values(), np.searchsorted(label_dict.keys(),y_test.argmax(1)))

样品运行-

In [79]: my_list
Out[79]: 
['airplane',
 'automobile',
 'bird',
 'cat',
 'deer',
 'dog',
 'frog',
 'horse',
 'ship',
 'truck']

In [80]: y_test
Out[80]: 
array([[ 0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.]])

In [81]: [my_list[i] for i in y_test.argmax(1)]
Out[81]: ['cat', 'automobile', 'ship']

In [82]: np.take(my_list,y_test.argmax(1))
Out[82]: 
array(['cat', 'automobile', 'ship'], 
      dtype='|S10')

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