tensorflow的Numpy数组。克拉斯。预处理。文本标记器。文本序列给出了奇怪的输出，列表（[2]），而不是[[2]]

Question

tensorflow的Numpy数组。克拉斯。预处理。文本标记器。文本\u到\u序列为培训标签提供奇怪的输出，如下所示：

(training_label_list[0:10]) = [list([1]) list([1]) list([1]) list([1]) list([1]) list([1]) list([1]) list([1]) list([1]) list([1])]

但是正在为验证标签打印普通数组，

(validation_label_list[0:10]) = [[16]
 [16]
 [16]
 [16]
 [16]
 [16]
 [16]
 [16]
 [16]
 [16]]

换句话说，type（training_label_list[0]）=

type(validation_label_list[0]) =  <class 'numpy.ndarray'>

因此，在使用Keras模型训练模型时。拟合，它会导致以下错误，

ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type list).

这是谷歌可乐的链接，很容易重现错误。

下面给出了重现错误的完整代码：

!pip install tensorflow==2.1

# For Preprocessing the Text => To Tokenize the Text
from tensorflow.keras.preprocessing.text import Tokenizer
# If the Two Articles are of different length, pad_sequences will make the length equal
from tensorflow.keras.preprocessing.sequence import pad_sequences

# Package for performing Numerical Operations
import numpy as np

Unique_Labels_List = ['India', 'USA', 'Australia', 'Germany', 'Bhutan', 'Nepal', 'New Zealand', 'Israel', 'Canada', 'France', 'Ireland', 'Poland', 'Egypt', 'Greece', 'China', 'Spain', 'Mexico']


Train_Labels = Unique_Labels_List[0:14]
#print('Train Labels = {}'.format(Train_Labels))

Val_Labels =  Unique_Labels_List[14:]
#print('Val_Labels = {}'.format(Val_Labels))

No_Of_Train_Items = [248, 200, 200, 218, 248, 248, 249, 247, 220, 200, 200, 211, 224, 209]
No_Val_Items = [212, 200, 219]

T_L = []
for Each_Label, Item in zip(Train_Labels, No_Of_Train_Items):
    T_L.append([Each_Label] * Item)

T_L = [item for sublist in T_L for item in sublist]

V_L = []
for Each_Label, Item in zip(Val_Labels, No_Val_Items):
    V_L.append([Each_Label] * Item)

V_L = [item for sublist in V_L for item in sublist]


len(T_L)

len(V_L)

label_tokenizer = Tokenizer()

label_tokenizer.fit_on_texts(Unique_Labels_List)

# Since it should be a Numpy Array, we should Convert the Sequences to Numpy Array, for both Training and 
# Test Labels

training_label_list = np.array(label_tokenizer.texts_to_sequences(T_L))

validation_label_list = np.array(label_tokenizer.texts_to_sequences(V_L))

print('(training_label_list[0:10]) = {}'.format((training_label_list[0:10])))
print('(validation_label_list[0:10]) = {}'.format((validation_label_list[0:10])))

print('type(training_label_list[0]) = ', type(training_label_seq[0]))
print('type(validation_label_seq[0]) = ', type(validation_label_seq[0]))

如果有人能建议我如何获得相同格式的培训标签和验证标签，我将不胜感激，因为我在这方面花了太多时间。




Answer 1

您的问题是，当您将训练数据转换为numpy数组时，特定的numpy数组由列表元素组成，因此会出现错误

ValueError:无法将NumPy数组转换为张量（不支持的对象类型列表）。

这个错误比它看起来更微妙；一些人报告说他们不得不从2.1.0切换回2.0.0。Numpy的数组（）和阿斯数组（）函数之间有什么区别？

我个人会试试这个：

1. 使用training_label_list=np.asarray（label_tokenizer.texts_to_sequences（T_L）），而不是np.array。Tensorflow-ValueError：无法将NumPy数组转换为Tensor（不支持的对象类型浮点）
2. 根据这个：

将列表列表放入numpy数组

你将不得不强制施展（虽然很奇怪，但这应该是可行的）：

x=[[1,2],[1,2,3],[1]]
y=numpy.array([numpy.array(xi) for xi in x])
type(y)
>>><type 'numpy.ndarray'>
type(y[0])
>>><type 'numpy.ndarray'>

在试图帮助你解决这个问题时，我发现了一个关于Numpy铸造的有趣事实：

案例1：

   my_list = [[1,2],[2],[3]]
   my_numpy_array = np.array(my_list)
   print(type(my_numpy_array))
   print(type(my_numpy_array[0]))
   <class 'numpy.ndarray'>
   <class 'list'>

案例2：

    my_list = [[1],[2],[3]]
    my_numpy_array = np.array(my_list)
    print(type(my_numpy_array))
    print(type(my_numpy_array[0]))
    <class 'numpy.ndarray'>
    <class 'numpy.ndarray'>

简短的结论：如果子列表的长度不同，显然它们被保留为列表，而不是转换为Numpy数组。

我对你的代码进行了测试，现在它可以工作了：

training_label_seq = np.asarray(label_tokenizer.texts_to_sequences(T_L))

training_label_seq = np.array([np.array(training_element) for training_element in training_label_seq])

validation_label_seq = np.asarray(label_tokenizer.texts_to_sequences(V_L))



print('(training_label_seq[0:10]) = {}'.format((training_label_seq[0:10])))
print('(validation_label_seq[0:10]) = {}'.format((validation_label_seq[0:10])))

print('type(training_label_list[0]) = ', type(training_label_seq[0]))
print('type(validation_label_seq[0]) = ', type(validation_label_seq[0]))



(training_label_seq[0:10]) = [array([1]) array([1]) array([1]) array([1]) array([1]) array([1])
 array([1]) array([1]) array([1]) array([1])]
(validation_label_seq[0:10]) = [[16]
 [16]
 [16]
 [16]
 [16]
 [16]
 [16]
 [16]
 [16]
 [16]]
type(training_label_list[0]) =  <class 'numpy.ndarray'>
type(validation_label_seq[0]) =  <class 'numpy.ndarray'>

Answer 2

替换np。数组带有np。正如Stack中提到的那样，Stack解决了这个问题。

现在，正确的输出是

(training_label_seq[0:10]) = [1 1 1 1 1 1 1 1 1 1]
(validation_label_seq[0:10]) = [16 16 16 16 16 16 16 16 16 16]
type(training_label_list[0]) =  <class 'numpy.int64'>
type(validation_label_seq[0]) =  <class 'numpy.int64'>

工作代码的链接在这个Google Colab中。

下面提到的是工作代码（以防上述链接不起作用）：

!pip install tensorflow==2.1

# For Preprocessing the Text => To Tokenize the Text
from tensorflow.keras.preprocessing.text import Tokenizer
# If the Two Articles are of different length, pad_sequences will make the length equal
from tensorflow.keras.preprocessing.sequence import pad_sequences

# Package for performing Numerical Operations
import numpy as np

Unique_Labels_List = ['India', 'USA', 'Australia', 'Germany', 'Bhutan', 'Nepal', 'New Zealand', 'Israel', 'Canada', 'France', 'Ireland', 'Poland', 'Egypt', 'Greece', 'China', 'Spain', 'Mexico']


Train_Labels = Unique_Labels_List[0:14]
#print('Train Labels = {}'.format(Train_Labels))

Val_Labels =  Unique_Labels_List[14:]
#print('Val_Labels = {}'.format(Val_Labels))

No_Of_Train_Items = [248, 200, 200, 218, 248, 248, 249, 247, 220, 200, 200, 211, 224, 209]
No_Val_Items = [212, 200, 219]

T_L = []
for Each_Label, Item in zip(Train_Labels, No_Of_Train_Items):
    T_L.append([Each_Label] * Item)

T_L = [item for sublist in T_L for item in sublist]

V_L = []
for Each_Label, Item in zip(Val_Labels, No_Val_Items):
    V_L.append([Each_Label] * Item)

V_L = [item for sublist in V_L for item in sublist]


len(T_L)

len(V_L)

label_tokenizer = Tokenizer()

label_tokenizer.fit_on_texts(Unique_Labels_List)

# Since it should be a Numpy Array, we should Convert the Sequences to Numpy Array, for both Training and 
# Test Labels

training_label_list = np.hstack(label_tokenizer.texts_to_sequences(T_L))

validation_label_list = np.hstack(label_tokenizer.texts_to_sequences(V_L))

print('(training_label_list[0:10]) = {}'.format((training_label_list[0:10])))
print('(validation_label_list[0:10]) = {}'.format((validation_label_list[0:10])))

print('type(training_label_list[0]) = ', type(training_label_seq[0]))
print('type(validation_label_seq[0]) = ', type(validation_label_seq[0]))