Python read_data_集合已弃用，将在将来的版本中删除。更新说明

这是我的密码

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)

这些是我运行它时收到的警告，我正在使用一个在线环境Google Colab来运行它

WARNING:tensorflow:From <ipython-input-2-0d6f173b16c0>:6: read_data_sets 
(from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and 
will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from 
tensorflow/models.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist- 
packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: 
maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is 
deprecated and will be removed in a future version.
Instructions for updating:
Please write your own downloading logic.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist- 
packages/tensorflow/contrib/learn/python/learn/datasets/base.py:252: 
_internal_retry.<locals>.wrap.<locals>.wrapped_fn (from 
tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will 
be removed in a future version.
Instructions for updating:
Please use urllib or similar directly.
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist- 
packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: 
extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is 
deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
Extracting /tmp/data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist- 
packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: 
extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is 
deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
Extracting /tmp/data/train-labels-idx1-ubyte.gz

警告：tensorflow:From:6:读取数据集
（来自tensorflow.contrib.learn.python.learn.datasets.mnist）已被弃用，并且
将在将来的版本中删除。
更新说明：
请使用诸如official/mnist/dataset.py之类的备选方案
tensorflow/模型。
警告：tensorflow:From/usr/local/lib/python3.6/dist-
packages/tensorflow/contrib/learn/python/learn/dataset/mnist.py:260：
也许您的下载（来自tensorflow.contrib.learn.python.learn.datasets.base）是
已弃用，将在将来的版本中删除。
更新说明：
请编写您自己的下载逻辑。
警告：tensorflow:From/usr/local/lib/python3.6/dist-
packages/tensorflow/contrib/learn/python/learn/datasets/base.py:252:
_内部\u重试..包装..包装\u fn（从
tensorflow.contrib.learn.python.learn.datasets.base）已被弃用，并将
将在将来的版本中删除。
更新说明：
请直接使用urllib或类似工具。
已成功下载train-images-idx3-ubyte.gz 9912422字节。
警告：tensorflow:From/usr/local/lib/python3.6/dist-
packages/tensorflow/contrib/learn/python/learn/dataset/mnist.py:262:
extract_图像（来自tensorflow.contrib.learn.python.learn.datasets.mnist）是
已弃用，将在将来的版本中删除。
更新说明：
请使用tf.data来实现此功能。
提取/tmp/data/train-images-idx3-ubyte.gz
已成功下载train-labels-idx1-ubyte.gz 28881字节。
警告：tensorflow:From/usr/local/lib/python3.6/dist-
packages/tensorflow/contrib/learn/python/learn/dataset/mnist.py:267:
extract_标签（来自tensorflow.contrib.learn.python.learn.datasets.mnist）是
已弃用，将在将来的版本中删除。
更新说明：
请使用tf.data来实现此功能。
提取/tmp/data/train-labels-idx1-ubyte.gz

非常感谢您的帮助

请使用以下代码

import tensorflow as tf
#Sets the threshold for what messages will be logged.
old_v = tf.logging.get_verbosity()
# able to set the logging verbosity to either DEBUG, INFO, WARN, ERROR, or FATAL. Here its ERROR
tf.logging.set_verbosity(tf.logging.ERROR)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
#in the end
tf.logging.set_verbosity(old_v)

---

现在推荐的方法似乎是使用keras：

from tensorflow import keras


mnist = tf.keras.datasets.mnist(train_images, train_labels),(test_images, test_labels) = mnist.load_data()

---

到底是什么问题？它确实包括更新代码的说明，字面意思是“请使用tf.data来实现此功能”。看看这个问题的答案：通常最好解释一个解决方案，而不是仅仅发布几行匿名代码。你会读书，你是认真的吗？！你真的认为隐藏警告是最好的解决办法吗？！哦，天哪……通常警告是有理由的！别把它藏起来！