我正在使用Tensorflow创建一个非常基础的人工智能，并且使用了官方文档/教程中的代码。以下是我的完整代码：
from __future__ import absolute_import, division, print_functionimport tensorflow as tffrom tensorflow import kerasimport matplotlib.pyplot as pltfashion_mnist = keras.datasets.fashion_mnist(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']train_images = train_images / 255.0train_labels = train_labels / 255.0plt.figure(figsize=(10,10))for i in range(25): plt.subplot(5,5,i+1) plt.xticks([]) plt.yticks([]) plt.grid(False) plt.imshow(train_images[i], cmap=plt.cm.binary) plt.xlabel(class_names[train_labels[i]])plt.show()问题出现在这一行：
plt.xlabel(class_names[train_labels[i]])TypeError: list indices must be integers or slices, not numpy.float64没问题，使用.item()将numpy.float64转换为int
plt.xlabel(class_names[train_labels[i.item()]])AttributeError: 'int' object has no attribute 'item'它原本就是int类型吗？
这是在Python 3.7上运行的，使用的是Tensorflow 1.13.1版本。
回答：
错误是由以下代码引起的：
train_labels = train_labels / 255.0train_labels是一个标签的ndarray。通过将其除以255，结果ndarray中包含浮点数。因此，一个浮点数被用作class_names的索引，导致了第一个错误。
list indices must be integers or slices, not numpy.float64
要将numpy数组x转换为int，可以使用x.astype(int)。但在这种情况下，这样做会创建一个所有值都为0的数组。
解决方法是删除上面标识的行：
from __future__ import absolute_import, division, print_functionimport tensorflow as tffrom tensorflow import kerasimport matplotlib.pyplot as pltfashion_mnist = keras.datasets.fashion_mnist(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']train_images = train_images / 255.0# train_labels = train_labels / 255.0plt.figure(figsize=(10,10))for i in range(25): print(train_labels[i], train_images.shape, train_labels.shape, type(train_labels)) plt.subplot(5,5,i+1) plt.xticks([]) plt.yticks([]) plt.grid(False) plt.imshow(train_images[i], cmap=plt.cm.binary) plt.xlabel(class_names[train_labels[i]])plt.show()