如何将列表转换为 numpy 数组

Question

这是合作https://colab.research.google.com/drive/1wftAvDu_Wu2Y9ahgI1Z1FLciUH5MnSJ9的链接

train_labels = ['GovernmentSchemes', 'GovernmentSchemes', 'GovernmentSchemes', 'GovernmentSchemes', 'CropInsurance']

training_label_seq = np.array(label_tokenizer.texts_to_sequences(train_labels))

output 来了：

[list([3]) list([3]) list([3]) ... list([2]) list([5]) list([1])]

预期 output：

[[3] [3] [3] .. [2] [5]...]

num_epochs = 30
history = model.fit(train_padded, training_label_seq, epochs=num_epochs, validation_data=(validation_padded, validation_label_seq))

错误 => ValueError：无法将 NumPy 数组转换为张量（不支持的 object 类型列表）

Answer 1

我能够使用以下代码重新创建您的问题 -

重新创建问题的代码 -

import numpy as np
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.preprocessing.text import Tokenizer

label_tokenizer = Tokenizer()

# Fit on a text 
fit_text = "Tensorflow warriors are awesome people"
label_tokenizer.fit_on_texts(fit_text)

# Training Labels
train_labels = "Tensorflow warriors are great people"
training_label_list = np.array(label_tokenizer.texts_to_sequences(train_labels))

# Print the 
print(training_label_list)
print(type(training_label_list))
print(type(training_label_list[0]))

Output -

2.2.0
[list([9]) list([1]) list([10]) list([5]) list([3]) list([2]) list([11])
 list([7]) list([3]) list([6]) list([]) list([6]) list([4]) list([2])
 list([2]) list([12]) list([3]) list([2]) list([5]) list([]) list([4])
 list([2]) list([1]) list([]) list([4]) list([2]) list([1]) list([])
 list([]) list([2]) list([1]) list([4]) list([9]) list([]) list([8])
 list([1]) list([3]) list([8]) list([7]) list([1])]
<class 'numpy.ndarray'>
<class 'list'>

解决方案 -

1. 用np.array替换np.hstack将解决您的问题。 您的model.fit()现在应该可以正常工作了。
2. 否则，如果您正在寻找预期的 output ， training_label_list = label_tokenizer.texts_to_sequences(train_labels)将为您提供一个列表列表。 您可以使用np.array([np.array(i) for i in training_label_list])转换为数组数组。 仅当您的列表列表包含具有相同数量元素的列表时，这才有效。

np.hstack Code -解决方案中第 1 点的代码。

import numpy as np
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.preprocessing.text import Tokenizer

label_tokenizer = Tokenizer()

# Fit on a text 
fit_text = "Tensorflow warriors are awesome people"
label_tokenizer.fit_on_texts(fit_text)

# Training Labels
train_labels = "Tensorflow warriors are great people"
training_label_list = np.hstack(label_tokenizer.texts_to_sequences(train_labels))

# Print the 
print(training_label_list)
print(type(training_label_list))
print(type(training_label_list[0]))

Output -

2.2.0
[ 9.  1. 10.  4.  2.  3. 11.  7.  2.  5.  5.  6.  3.  3. 12.  2.  3.  4.
  6.  3.  1.  3.  1.  6.  9.  8.  1.  2.  8.  7.  1.]
<class 'numpy.ndarray'>
<class 'numpy.float64'>

预期 output 有问题 -解决方案中第 2 点的代码。

import numpy as np
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.preprocessing.text import Tokenizer

label_tokenizer = Tokenizer()

# Fit on a text 
fit_text = "Tensorflow warriors are awesome people"
label_tokenizer.fit_on_texts(fit_text)

# Training Labels
train_labels = "Tensorflow warriors are great people"
training_label_list = label_tokenizer.texts_to_sequences(train_labels)

# Print 
print(training_label_list)
print(type(training_label_list))
print(type(training_label_list[0]))

# To convert elements to array
training_label_list = np.array([np.array(i) for i in training_label_list])

# Print
print(training_label_list)
print(type(training_label_list))
print(type(training_label_list[0]))

Output -

2.2.0
[[9], [1], [10], [4], [2], [3], [11], [7], [2], [5], [], [5], [6], [3], [3], [12], [2], [3], [4], [], [6], [3], [1], [], [], [3], [1], [6], [9], [], [8], [1], [2], [8], [7], [1]]
<class 'list'>
<class 'list'>
[array([9]) array([1]) array([10]) array([4]) array([2]) array([3])
 array([11]) array([7]) array([2]) array([5]) array([], dtype=float64)
 array([5]) array([6]) array([3]) array([3]) array([12]) array([2])
 array([3]) array([4]) array([], dtype=float64) array([6]) array([3])
 array([1]) array([], dtype=float64) array([], dtype=float64) array([3])
 array([1]) array([6]) array([9]) array([], dtype=float64) array([8])
 array([1]) array([2]) array([8]) array([7]) array([1])]
<class 'numpy.ndarray'>
<class 'numpy.ndarray'>

希望这能回答你的问题。 快乐学习。

---

更新 2/6/2020 - Anirudh_k07 ，根据我们的讨论，我查看了您的程序，在使用np.hstack作为标签后，您在model.fit()中遇到错误。

ValueError: Data cardinality is ambiguous:
  x sizes: 41063
  y sizes: 41429
Please provide data which shares the same first dimension.

您收到此错误是因为很少有标签具有-和/等特殊字符。 因此，在执行np.hstack(label_tokenizer.texts_to_sequences(train_labels)时，他们正在创建额外的行。您可以使用print(set(train_labels))唯一train_labels列表。

这是我想说的要点-

# These Labels have special character
train_labels = ['Bio-PesticidesandBio-Fertilizers','Old/SenileOrchardRejuvenation']
training_label_seq = np.hstack(label_tokenizer.texts_to_sequences(train_labels))
print("Two labels are converted to Five :",training_label_seq)

# These Labels are fine
train_labels = ['SoilHealthCard', 'PostHarvestPreservation', 'FertilizerUseandAvailability']
training_label_seq = np.hstack(label_tokenizer.texts_to_sequences(train_labels))
print("Three labels are remain three :",training_label_seq)

Output -

Two labels are converted to Five : [17 18 19 51 52]
Three labels are remain three : [20 36  5]

因此，请进行适当的预处理并消除train_labels中的这些特殊字符，然后在标签上使用np.hstack(label_tokenizer.texts_to_sequences(train_labels)) 。 之后，您的model.fit()应该可以正常工作。

希望这能回答你的问题。 快乐学习。