Question

我有一个如此定义的数据框我正在尝试为深度学习问题创建序列标签输入。所以我为每个句子元素都有标签，我为句子元素创建WordIndex的向量，为10个维度填充它们，对句子元素的标签执行相同的操作（为标签创建TagIndex，将它们填充到10尺寸）。然后我需要将TagIndices转换为分类变量。那是错误出现的时候。任何帮助都会很棒。这是正确的做法吗？

SentenceID = c(1,1,1,1,2,2,2,3,3,3,3,3,3,3,3)
Tokens = c("I","went","to","school","nobody","can","find","some","people","know","what","they","are","doing","now")
WordIndex = c(3,4,7,8,9,10,12,54,34,66,33,89,87,23,22)
TagIndex = c(1,3,2,4,1,3,4,1,2,4,3,4,2,3,4)

df = data.frame(SentenceID, Tokens, WordIndex, TagIndex)

lst <- split(df$WordIndex, f = df$SentenceID)

lstWord2 <- lapply(lst, function(x){
  if (length(x) < 10){
    x2 <- c(x, rep(0, 10 - length(x)))
  }
  return(x2)
})

lstTag <- split(df$TagIndex, f = df$SentenceID)

lstTag2 <- lapply(lstTag, function(x){
  if (length(x) < 10){
    x2 <- c(x, rep(0, 10 - length(x)))
  }
  return(x2)
})

is.vector(lstTag2)

y <- to_categorical(lstTag2, num_classes = NULL)

我得到的错误就是这个。

Error in py_call_impl(callable, dots$args, dots$keywords) : 
  TypeError: int() argument must be a string, a bytes-like object or a number, not 'dict'

Detailed traceback: 
  File "C:\Users\balak\AppData\Local\conda\conda\envs\R-TENS~1\lib\site-packages\keras\utils\np_utils.py", line 22, in to_categorical
    y = np.array(y, dtype='int')

Answer 1

我想to_categorical函数要求输入是一个矩阵，这样做可以使它起作用：

SentenceID = c(1,1,1,1,2,2,2,3,3,3,3,3,3,3,3)
Tokens = c("I","went","to","school","nobody","can","find","some","people","know","what","they","are","doing","now")
WordIndex = c(3,4,7,8,9,10,12,54,34,66,33,89,87,23,22)
TagIndex = c(1,3,2,4,1,3,4,1,2,4,3,4,2,3,4)

df = data.frame(SentenceID, Tokens, WordIndex, TagIndex)

lst <- split(df$WordIndex, f = df$SentenceID)

lstWord2 <- lapply(lst, function(x){
  if (length(x) < 10){
    x2 <- c(x, rep(0, 10 - length(x)))
  }
  return(x2)
})

lstTag <- split(df$TagIndex, f = df$SentenceID)

lstTag2 <- lapply(lstTag, function(x){
  if (length(x) < 10){
    x2 <- c(x, rep(0, 10 - length(x)))
  }
  return(x2)
})


y <- to_categorical(as.matrix(lstTag2), num_classes = NULL)

我明白了：

> y
, , 1

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]    0    0    0    0    1    1    1    1    1     1
[2,]    0    0    0    1    1    1    1    1    1     1
[3,]    0    0    0    0    0    0    0    0    1     1

, , 2

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]    1    0    0    0    0    0    0    0    0     0
[2,]    1    0    0    0    0    0    0    0    0     0
[3,]    1    0    0    0    0    0    0    0    0     0

, , 3

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]    0    0    1    0    0    0    0    0    0     0
[2,]    0    0    0    0    0    0    0    0    0     0
[3,]    0    1    0    0    0    1    0    0    0     0

, , 4

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]    0    1    0    0    0    0    0    0    0     0
[2,]    0    1    0    0    0    0    0    0    0     0
[3,]    0    0    0    1    0    0    1    0    0     0

, , 5

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]    0    0    0    1    0    0    0    0    0     0
[2,]    0    0    1    0    0    0    0    0    0     0
[3,]    0    0    1    0    1    0    0    1    0     0

R to_categorical用于使用Keras进行序列标记

1 个答案: