如何在tensorflow中设置屏蔽特定输出的日志信息？
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本文共计1047个文字，预计阅读时间需要5分钟。
在TensorFlow中，可以通过设置环境变量 `TF_CPP_MIN_LOG_LEVEL` 来控制是否输出警告、信息和错误等信息。具体步骤如下：
1. 导入必要的模块：pythonimport osimport tensorflow as tf
2. 设置环境变量：pythonos.environ['TF_CPP_MIN_LOG_LEVEL']='2'其中，`'2'` 表示仅输出错误信息。
3. （可选）使用TensorFlow进行计算或操作。
通过上述设置，可以减少TensorFlow在运行时输出的日志信息，从而避免不必要的干扰。
tensorflow中可以通过配置环境变量 'TF_CPP_MIN_LOG_LEVEL' 的值，控制tensorflow是否屏蔽通知信息、警告、报错等输出信息。
使用方法：
import os import tensorflow as tf os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # or any {'0', '1', '2'}
TF_CPP_MIN_LOG_LEVEL 取值 0 ： 0也是默认值，输出所有信息
TF_CPP_MIN_LOG_LEVEL 取值 1 ： 屏蔽通知信息
TF_CPP_MIN_LOG_LEVEL 取值 2 ： 屏蔽通知信息和警告信息
TF_CPP_MIN_LOG_LEVEL 取值 3 ： 屏蔽通知信息、警告信息和报错信息
测试代码：
import tensorflow as tf import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0' # os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' # os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' v1 = tf.constant([1.0, 2.0, 3.0], shape=[3], name='v1') v2 = tf.constant([1.0, 2.0, 3.0], shape=[3], name='v2') sumV12 = v1 + v2 with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess: print sess.run(sumV12)
TF_CPP_MIN_LOG_LEVEL 为 0 的输出：
2018-04-21 14:59:09.910415: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910442: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910448: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910453: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910457: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.911260: I tensorflow/core/common_runtime/direct_session.cc:300] Device mapping: 2018-04-21 14:59:09.911816: I tensorflow/core/common_runtime/simple_placer.cc:872] add: (Add)/job:localhost/replica:0/task:0/cpu:0 2018-04-21 14:59:09.911835: I tensorflow/core/common_runtime/simple_placer.cc:872] v2: (Const)/job:localhost/replica:0/task:0/cpu:0 2018-04-21 14:59:09.911841: I tensorflow/core/common_runtime/simple_placer.cc:872] v1: (Const)/job:localhost/replica:0/task:0/cpu:0 Device mapping: no known devices. add: (Add): /job:localhost/replica:0/task:0/cpu:0 v2: (Const): /job:localhost/replica:0/task:0/cpu:0 v1: (Const): /job:localhost/replica:0/task:0/cpu:0 [ 2. 4. 6.]
值为0也是默认的输出，分为三部分，一个是警告信息说没有优化加速，二是通知信息告知操作所用的设备，三是程序中代码指定要输出的结果信息
TF_CPP_MIN_LOG_LEVEL 为 1 的输出，没有通知信息了： 2018-04-21 14:59:09.910415: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910442: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910448: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910453: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910457: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. Device mapping: no known devices. add: (Add): /job:localhost/replica:0/task:0/cpu:0 v2: (Const): /job:localhost/replica:0/task:0/cpu:0 v1: (Const): /job:localhost/replica:0/task:0/cpu:0 [ 2. 4. 6.]
TF_CPP_MIN_LOG_LEVEL 为 2和3 的输出，设置为2就没有警告信息了，设置为3警告和报错信息（如果有）就都没有了：
Device mapping: no known devices. add: (Add): /job:localhost/replica:0/task:0/cpu:0 v2: (Const): /job:localhost/replica:0/task:0/cpu:0 v1: (Const): /job:localhost/replica:0/task:0/cpu:0 [ 2. 4. 6.]
以上这篇在tensorflow中实现屏蔽输出的log信息就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持易盾网络。
本文共计1047个文字，预计阅读时间需要5分钟。
在TensorFlow中，可以通过设置环境变量 `TF_CPP_MIN_LOG_LEVEL` 来控制是否输出警告、信息和错误等信息。具体步骤如下：
1. 导入必要的模块：pythonimport osimport tensorflow as tf
2. 设置环境变量：pythonos.environ['TF_CPP_MIN_LOG_LEVEL']='2'其中，`'2'` 表示仅输出错误信息。
3. （可选）使用TensorFlow进行计算或操作。
通过上述设置，可以减少TensorFlow在运行时输出的日志信息，从而避免不必要的干扰。
tensorflow中可以通过配置环境变量 'TF_CPP_MIN_LOG_LEVEL' 的值，控制tensorflow是否屏蔽通知信息、警告、报错等输出信息。
使用方法：
import os import tensorflow as tf os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # or any {'0', '1', '2'}
TF_CPP_MIN_LOG_LEVEL 取值 0 ： 0也是默认值，输出所有信息
TF_CPP_MIN_LOG_LEVEL 取值 1 ： 屏蔽通知信息
TF_CPP_MIN_LOG_LEVEL 取值 2 ： 屏蔽通知信息和警告信息
TF_CPP_MIN_LOG_LEVEL 取值 3 ： 屏蔽通知信息、警告信息和报错信息
测试代码：
import tensorflow as tf import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0' # os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' # os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' v1 = tf.constant([1.0, 2.0, 3.0], shape=[3], name='v1') v2 = tf.constant([1.0, 2.0, 3.0], shape=[3], name='v2') sumV12 = v1 + v2 with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess: print sess.run(sumV12)
TF_CPP_MIN_LOG_LEVEL 为 0 的输出：
2018-04-21 14:59:09.910415: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910442: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910448: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910453: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910457: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.911260: I tensorflow/core/common_runtime/direct_session.cc:300] Device mapping: 2018-04-21 14:59:09.911816: I tensorflow/core/common_runtime/simple_placer.cc:872] add: (Add)/job:localhost/replica:0/task:0/cpu:0 2018-04-21 14:59:09.911835: I tensorflow/core/common_runtime/simple_placer.cc:872] v2: (Const)/job:localhost/replica:0/task:0/cpu:0 2018-04-21 14:59:09.911841: I tensorflow/core/common_runtime/simple_placer.cc:872] v1: (Const)/job:localhost/replica:0/task:0/cpu:0 Device mapping: no known devices. add: (Add): /job:localhost/replica:0/task:0/cpu:0 v2: (Const): /job:localhost/replica:0/task:0/cpu:0 v1: (Const): /job:localhost/replica:0/task:0/cpu:0 [ 2. 4. 6.]
值为0也是默认的输出，分为三部分，一个是警告信息说没有优化加速，二是通知信息告知操作所用的设备，三是程序中代码指定要输出的结果信息
TF_CPP_MIN_LOG_LEVEL 为 1 的输出，没有通知信息了： 2018-04-21 14:59:09.910415: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910442: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910448: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910453: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 2018-04-21 14:59:09.910457: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. Device mapping: no known devices. add: (Add): /job:localhost/replica:0/task:0/cpu:0 v2: (Const): /job:localhost/replica:0/task:0/cpu:0 v1: (Const): /job:localhost/replica:0/task:0/cpu:0 [ 2. 4. 6.]
TF_CPP_MIN_LOG_LEVEL 为 2和3 的输出，设置为2就没有警告信息了，设置为3警告和报错信息（如果有）就都没有了：
Device mapping: no known devices. add: (Add): /job:localhost/replica:0/task:0/cpu:0 v2: (Const): /job:localhost/replica:0/task:0/cpu:0 v1: (Const): /job:localhost/replica:0/task:0/cpu:0 [ 2. 4. 6.]
以上这篇在tensorflow中实现屏蔽输出的log信息就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持易盾网络。