这是协作的链接https://colab.research.google.com/drive/1wftAvDu_Wu2Y9ahgI1Z1FLciUH5MnSJ9
train_labels = ['GovernmentSchemes','GovernmentSchemes','GovernmentSchemes','GovernmentSchemes','CropInsurance']
training_label_seq = np.array(label_tokenizer.texts_to_sequences(train_labels))
输出即将到来:
[list([3]) list([3]) list([3]) ... list([2]) list([5]) list([1])]
预期产量:
[[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(不支持的对象类型列表)
我可以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]))
输出-
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-
np.array
为np.hstack
可以解决您的问题。您model.fit()
现在应该可以正常工作了。training_label_list = label_tokenizer.texts_to_sequences(train_labels)
将列出列表。您可以使用np.array([np.array(i) for i in training_label_list])
转换为array数组。仅当您的列表列表包含元素数量相同的列表时,此方法才有效。np.hstack代码-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]))
输出-
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'>
预期的输出有问题-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]))
输出-
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'>
希望这能回答您的问题。学习愉快。
更新2 / 6 / 2020 - Anirudh_k07,根据我们的讨论,我调查了您的程序,model.fit()
使用np.hstack
标签后出现以下错误。
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特殊字符,如-
和/
。因此,在执行时np.hstack(label_tokenizer.texts_to_sequences(train_labels)
,他们正在创建其他行。您可以train_labels
使用来打印唯一列表print(set(train_labels))
。
这是我要说的要点-
# 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)
输出-
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其中的特殊字符train_labels
,然后np.hstack(label_tokenizer.texts_to_sequences(train_labels))
在标签上使用。model.fit()
之后,您应该可以正常工作。
希望这能回答您的问题。学习愉快。
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