Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

由于现在神经网络这个东西比较火,准确的说是深度学习这个东西比较火,我们实验室准备靠这个东西发几个CCF A类的文章,虽然我不太懂这东西,兴趣也一般都是毕竟要跟随主流的,于是今天安装起了 Tensorflow 这个深度学习的框架. 安装好以后运行一个Demo ,如下: import tensorflow as tf a=tf.constant(2) b=tf.constant(20) with tf.Session() as sess: print(sess.run(a*b)) 运行结果如下: 2

在导入tensorflow后,进行运算时,出现了报错Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 解决方法:在最顶行写入以下代码即可 import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' 参考资料:Advanced Vector Extensions import os os.environ["TF_CPP_M

我不知道大家是不是和我一样是用pip install tensorflow安装的tensorflow库,但是使用的时候,却会显示: Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 虽然运行起来没什么问题,但是看着总是不舒服.网上查阅了其他大神的博客之后,终于知道怎么处理了. 首先,卸载原有的Tensorflow 包: pip unistall tensorfl

为了提升CPU计算速度的.若你有支持cuda的GPU,则可以忽略这个问题,因为安装SSE4.1, SSE4.2, AVX, AVX2, FMA, 仅仅提升CPU的运算速度(大概有3倍). 解决方法: 忽视警告,并屏蔽警告 开头输入如下: import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' 2.进 tensorflow 官网,从源码安装. 原文地址:https://www.cnblogs.com/theWinter/p/8244685.html

TensorFlow指定GPU/CPU进行训练和输出devices信息 1.在tensorflow代码中指定GPU/CPU进行训练 with tf.device('/gpu:0'): .... with tf.device('/gpu:1'): ... with tf.device('/cpu:0'): ... 2.输出devices的信息 在指定devices的时候往往不知道具体的设备信息,这时可用下面的代码查看对应的信息 进入Python环境 from tensorflow.python.c

进入到Python环境 import tensorflow as tf tf.__version__ # 查看版本 tf.__path__ # 查看安装路径 查看TensorFlow版本的另一种方法 sudo pip3 show tensorflow-gpu # GPU版 sudo pip3 show tensorflow # 非GPU版 查看TensorFlow版本的另一种方法 $ python Python 3.6.7 (default, Oct 22 2018, 11:32:17) [GC

Fashion MNIST https://www.kaggle.com/zalando-research/fashionmnist Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, asso

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Dec 23 11:26:04 2018 @author: myhaspl """ import tensorflow as tf #创建图 c=tf.constant(0.0) g=tf.Graph() with g.as_default(): c1=tf.constant(0.1) g2=tf.get_defau