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提示請(qǐng)使用tf.data來(lái)實(shí)現(xiàn)此功能,怎么搞

D:\Anaconda3\python.exe F:/Python/mnist_testdemo/mnist/regression.py

D:\Anaconda3\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.

? from ._conv import register_converters as _register_converters

WARNING:tensorflow:From F:/Python/mnist_testdemo/mnist/regression.py:4: 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 D:\Anaconda3\lib\site-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 D:\Anaconda3\lib\site-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.

Extracting MNIST_data\train-images-idx3-ubyte.gz

WARNING:tensorflow:From D:\Anaconda3\lib\site-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.


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這里報(bào)的是個(gè)警告,說(shuō)這個(gè)mnist數(shù)據(jù)接口在后續(xù)版本里會(huì)刪除掉。不影響正常使用的。

0 回復(fù) 有任何疑惑可以回復(fù)我~

多運(yùn)行兩次就可以了

0 回復(fù) 有任何疑惑可以回復(fù)我~

可以先把TensorFlow降級(jí),弄到1.5左右應(yīng)該可以。tf.data是相對(duì)新一些的API,或許是不兼容

0 回復(fù) 有任何疑惑可以回復(fù)我~

我的跟你的差不多下載數(shù)據(jù)集時(shí)一堆警告,不知道什么原因

0 回復(fù) 有任何疑惑可以回復(fù)我~

首先看一下你的數(shù)據(jù)是否完全下載成功,懷疑是數(shù)據(jù)下載的問(wèn)題

0 回復(fù) 有任何疑惑可以回復(fù)我~

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提示請(qǐng)使用tf.data來(lái)實(shí)現(xiàn)此功能,怎么搞

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