如何將列表轉換為 numpy 數組

Question

這是合作https://colab.research.google.com/drive/1wftAvDu_Wu2Y9ahgI1Z1FLciUH5MnSJ9的鏈接

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

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

output 來了：

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

預期 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))

錯誤 => ValueError：無法將 NumPy 數組轉換為張量（不支持的 object 類型列表）

Answer 1

我能夠使用以下代碼重新創建您的問題 -

重新創建問題的代碼 -

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'>

解決方案 -

1. 用np.array替換np.hstack將解決您的問題。 您的model.fit()現在應該可以正常工作了。
2. 否則，如果您正在尋找預期的 output ， training_label_list = label_tokenizer.texts_to_sequences(train_labels)將為您提供一個列表列表。 您可以使用np.array([np.array(i) for i in training_label_list])轉換為數組數組。 僅當您的列表列表包含具有相同數量元素的列表時，這才有效。

np.hstack Code -解決方案中第 1 點的代碼。

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'>

預期 output 有問題 -解決方案中第 2 點的代碼。

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'>

希望這能回答你的問題。 快樂學習。

---

更新 2/6/2020 - Anirudh_k07 ，根據我們的討論，我查看了您的程序，在使用np.hstack作為標簽后，您在model.fit()中遇到錯誤。

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

您收到此錯誤是因為很少有標簽具有-和/等特殊字符。 因此，在執行np.hstack(label_tokenizer.texts_to_sequences(train_labels)時，他們正在創建額外的行。您可以使用print(set(train_labels))唯一train_labels列表。

這是我想說的要點-

# 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，請進行適當的預處理並消除train_labels中的這些特殊字符，然后在標簽上使用np.hstack(label_tokenizer.texts_to_sequences(train_labels)) 。 之后，您的model.fit()應該可以正常工作。

希望這能回答你的問題。 快樂學習。