Question

我有一个字符串列表，用作我的分类问题（使用卷积神经网络进行图像识别）的标签。这些标签由5-8个字符组成（0到9之间的数字和A到Z的字母）。为了训练我的神经网络，我想对标签进行热编码。我编写了编码一个标签的代码，但是在尝试将代码应用于列表时仍然遇到困难。

这是我的一个标签正常工作的代码：

com.glide.slider.library.SliderLayout;

我现在想获得相同的标签列表输出，并将输出存储在新列表中：

from numpy import argmax
# define input string
data = '7C24698'
print(data)
# define universe of possible input values
characters = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ '
# define a mapping of chars to integers
char_to_int = dict((c, i) for i, c in enumerate(characters))
int_to_char = dict((i, c) for i, c in enumerate(characters))
# integer encode input data
integer_encoded = [char_to_int[char] for char in data]
print(integer_encoded)
# one hot encode
onehot_encoded = list()
for value in integer_encoded:
    character = [0 for _ in range(len(characters))]
    character[value] = 1
    onehot_encoded.append(character)
print(onehot_encoded)
# invert encoding
inverted = int_to_char[argmax(onehot_encoded[0])]
print(inverted)

我该怎么做？

Answer 1

您可以使用LabelBinarizer from scikit-learn：

from sklearn.preprocessing import LabelBinarizer

>>> labels = ["first", "second", "third"]
>>> lb = LabelBinarizer()
>>> lb.fit(labels)
>>> lb.transform(labels)
array([[1, 0, 0],
       [0, 1, 0],
       [0, 0, 1]])

并将一键编码的标签转换回string值：

>>> encoded_labels = [
  [1, 0, 0],
  [0, 1, 0],
  [0, 0, 1]
]
>>> lb.inverse_transform(encoded_labels)
array(['first', 'second', 'third'])

Answer 2

您可以使用工作代码创建一个函数，然后使用内置函数map来申请lists_of_labels的一键编码函数中的每个元素：

from numpy import argmax
# define input string

def my_onehot_encoded(data):
    # define universe of possible input values
    characters = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ '
    # define a mapping of chars to integers
    char_to_int = dict((c, i) for i, c in enumerate(characters))
    int_to_char = dict((i, c) for i, c in enumerate(characters))
    # integer encode input data
    integer_encoded = [char_to_int[char] for char in data]
    # one hot encode
    onehot_encoded = list()
    for value in integer_encoded:
        character = [0 for _ in range(len(characters))]
        character[value] = 1
        onehot_encoded.append(character)

    return onehot_encoded


list_of_labels = ['7C24698', 'NDK745']
encoded_labels = list(map(my_onehot_encoded, list_of_labels))

一个热编码字符串列表

2 个答案: