通过调试信息,我指的是TensorFlow在我的终端中显示的关于加载的库和找到的设备等的信息,而不是Python错误。

I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:900] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties: 
name: Graphics Device
major: 5 minor: 2 memoryClockRate (GHz) 1.0885
pciBusID 0000:04:00.0
Total memory: 12.00GiB
Free memory: 11.83GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:717] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Graphics Device, pci bus id: 0000:04:00.0)
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 1.0KiB
...

当前回答

以上的解决方案都不能解决我在Jupyter Notebook中的问题,所以我使用以下来自Cicoria的代码片段,问题解决了。

import warnings  
with warnings.catch_warnings():  
    warnings.filterwarnings("ignore",category=FutureWarning)
    import tensorflow as tf
    from tensorflow import keras
    from tensorflow.keras.preprocessing.text import Tokenizer

print('Done') 

其他回答

以上的解决方案都不能解决我在Jupyter Notebook中的问题,所以我使用以下来自Cicoria的代码片段,问题解决了。

import warnings  
with warnings.catch_warnings():  
    warnings.filterwarnings("ignore",category=FutureWarning)
    import tensorflow as tf
    from tensorflow import keras
    from tensorflow.keras.preprocessing.text import Tokenizer

print('Done') 

献给那些还在努力获得操作系统的人。environ解决方案的工作,检查这是放在你导入tensorflow在你的脚本,就像mwweb的答案:

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # or any {'0', '1', '2'}
import tensorflow as tf

大多数答案在这里工作,但你必须使用他们每次你打开一个新的会话(例如与JupyterLab)。要使更改生效,必须设置环境变量。

Linux:

export TF_CPP_MIN_LOG_LEVEL="3"

(也可以将上面的行添加到.bashrc中,使更改永久,而不仅仅是针对会话)

窗口:

setx TF_CPP_MIN_LOG_LEVEL "3"

两者都为用户设置环境变量。

因为TF_CPP_MIN_LOG_LEVEL对我没用,你可以试试:

tf.logging.set_verbosity(tf.logging.WARN)

在tensorflow v1.6.0中为我工作过

为了兼容Tensorflow 2.0,你可以使用tf.get_logger

import logging
tf.get_logger().setLevel(logging.ERROR)