TensorFlow问题“The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.”

【TensorFlow问题“The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.”】的更多相关文章

TensorFlow问题“The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.”

出现的问题: 在使用TensorFlow跑官方教程例子时报以下warning: 虽程序能正常跑出结果,但作为一名强迫症患者对此很是不爽,于是查找资料找到隐藏该warning的解决办法. 解决办法: 在程序中加入以下代码: import os os.environ['TF_CPP_MIN_LOG_LEVEL']='2' 解决结果: import os os.environ['TF_CPP_MIN_LOG_LEVEL']='1' # 这是默认的显示等级,显示所有信息 # 2级 import os o…

TensorFlow问题:The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.

1. 问题描述 The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 2. 解决办法 Those are simply warnings. They are just informing you if you build TensorFlow from source…

最简单的 TensorFlow 代码，TensorFlow Hello World 。

# -*- coding:utf-8 -*- from __future__ import print_function ''' HelloWorld example using TensorFlow library. Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' ''' 简单 helloworld 使用 TensorFlow 创建一个常量 op 这个 op 作为…

移动端目标识别(３)——使用TensorFlow Lite将tensorflow模型部署到移动端(ssd)之Running on mobile with TensorFlow Lite　（写的很乱，回头更新一个简洁的版本）

承接移动端目标识别(2) 使用TensorFlow Lite在移动设备上运行在本节中,我们将向您展示如何使用TensorFlow Lite获得更小的模型,并允许您利用针对移动设备优化的操作. TensorFlow Lite是TensorFlow针对移动和嵌入式设备的轻量级解决方案.它支持端上的机器学习推理,具有低延迟和小二进制模型大小. TensorFlow Lite使用了许多技术,例如允许更小和更快(定点数学)模型的量化内核. 对于本节,您需要从源代码构建TensorFlow…

移动端目标识别(1)——使用TensorFlow Lite将tensorflow模型部署到移动端(ssd)之TensorFlow Lite简介

平时工作就是做深度学习,但是深度学习没有落地就是比较虚,目前在移动端或嵌入式端应用的比较实际,也了解到目前主要有 caffe2,腾讯ncnn,tensorflow,因为工作用tensorflow比较多,所以也就从tensorflow上下手了. 下面内容主要参考&翻译: https://www.tensorflow.org/mobile/?hl=zh-cn https://github.com/tensorflow/models/blob/master/research/object_detect…