tensorflow keras 关闭打印warning

通过设置环境变量TF_CPP_MIN_LOG_LEVEL为3,并使用tf.get_logger().setLevel(ERROR),可以确保在TensorFlow2.3中屏蔽所有警告信息,只显示错误级别日志。

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用下面代码设置TensorFlow的日志级别为ERROR,以及将环境变量TF_CPP_MIN_LOG_LEVEL设置为'3',这将禁用TensorFlow的所有警告信息。os.environ部分一定要放在import tensorflow之前,我用的是tensorflow2.3,不加tf.get_logger().setLevel('ERROR')也会不生效。

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

import tensorflow as tf
tf.get_logger().setLevel('ERROR')
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"]='1'
# 默认的显示等级,显示所有信息
os.environ["TF_CPP_MIN_LOG_LEVEL"]='2'
# 只显示 warning 和 Error  
os.environ["TF_CPP_MIN_LOG_LEVEL"]='3'
# 只显示 Error 

``` !mkdir -p ~/.keras/datasets !cp work/mnist.npz ~/.keras/datasets/ import warnings warnings.filterwarnings("ignore") from keras.datasets import mnist #train_images 和 train_labels 组成了训练集(training set),模型将从这些数据中进行学习。 #然后在测试集(test set,即 test_images 和 test_labels)上对模型进行测试。 (train_images, train_labels), (test_images, test_labels) = mnist.load_data() train_images.shape#看下数据的维度 len(train_labels) train_labels test_images.shape len(test_labels) test_labels from keras import models from keras import layers # 构建神经网络模型 network = models.Sequential() network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,))) network.add(layers.Dense(10, activation='softmax')) network.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) train_images = train_images.reshape((60000, 28 * 28)) train_images = train_images.astype('float32') / 255 test_images = test_images.reshape((10000, 28 * 28)) test_images = test_images.astype('float32') / 255 from keras.utils import to_categorical train_labels = to_categorical(train_labels) test_labels = to_categorical(test_labels) network.fit(train_images, train_labels, epochs=5, batch_size=128) test_loss, test_acc = network.evaluate(test_images, test_labels) print('test_acc:', test_acc)```cp: cannot stat 'work/mnist.npz': No such file or directory;Using TensorFlow backend.;WARNING: Logging before flag parsing goes to stderr. W0407 05:07:41.395064 139824211502912 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead. W0407 05:07:41.399161 139824211502912 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead. W0407 05:07:41.404188 139824211502912 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
最新发布
04-08
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