我使用的是tensorflow1.10.1
创作声明:本文部分内容由AI辅助生成(AIGC),仅供参考
Python内容推荐
tensorflow 1.8 python36 win10.zip
亲测有用,配置情况大家可以去看我写的文章,经过很多次踩坑得到的经验教训,python 3.6 win10 系统。哈哈哈,心痛哦
TensorFlow1.10.0+64位+Linux版+Win版2份(Python3.6).rar
内置TensorFlow1.10.0的Linux版和Win版2个链接,兼容TensorFlow和Python3.6配套的问题,兼容Keras等库
中秋节Python特色主题课,编程课专用
里面有音乐文档,有图片,有背景,有源文件,有课件,很齐全,需要孩子接触过turtle。里面的内容大部分都是用Turtle写的。可以完整上一节课。建议课程是1.5~2小时
CSDN首页
发布文章
CSDN同步助手
复现遗传算法考虑储能和可再生能源消纳责任制的售电公司购售电策略(Python代码实现)
47 100
摘要:会在推荐、列表等场景外露,帮助读
内容概要:本文基于Matlab代码实现,研究飞机能量-机动性(E-M)特性,重点评估飞机的最大转弯速度(即机动速度)、最大可持续转弯速度以及最大可持续载荷系数所对应的真空速度。通过对飞机气动、推进和质量等参数的建模,结合飞行力学原理,构建性能分析模型,并通过Matlab仿真计算得出关键机动性能指标,为飞机飞行性能评估与优化提供技术支持。该研究不仅涵盖理论建模与公式推导,还提供了完整的可执行代码,便于用户复现结果并根据具体机型进行参数调整与扩展应用。; 适合人群:具备飞行器设计、航空工程或相关专业背景,熟悉Matlab编程与基本飞行力学理论的科研人员、工程师及研究生。; 使用场景及目标:①用于飞机总体设计阶段的机动性能快速评估;②辅助飞行员训练与飞行手册编制,明确关键飞行速度界限;③为飞行控制系统的设计与验证提供性能边界依据。; 其他说明:文中提供的Matlab代码可直接运行,便于读者复现结果并根据具体机型参数进行修改和扩展,具有较强的工程实用性和教学参考价值。
tensorflow-1.10.zip
这个是我用bazel-0.15.0(aarch64)版编译出来的Tensorflow-1.10(aarch64)动态链接库,可以用在ubuntu18 arm64 上进行深度学习
tensorflow 1.10.0 docs
TensorFlow documentation 1.10.0 的帮助文档,markdown文件。
TensorFlow1.2版本CIFAR10代码
TensorFlow1.2版本CIFAR10代码,之前网上的版本基本上都不能用了 MacOS升级TensorFlow1.2: pip install --upgrade tensorflow
cifar10 for Tensorflow1.11
Google官网教程,从中文社区下下来之后,根据1.11版本的Tensorflow进行了修改,遇到问题是gpu版本依然跑在cpu上,不过能跑,
Windows10下Anaconda安装tensorflow2.1.0
Windows10下Anaconda安装tensorflow2.1.0 笔者最近在安装tensorflow2.1.0,有一些心得,仅供大家参考。 首先是电脑环境,笔者电脑有 Anaconda3\Pycharm\Visual Studio 2019\CUDA 10.1\cuDNN10.1 Anaconda官网 https://www.anaconda.com/distribution 安装方法有很多可以自行搜索,我的建议就是安装步骤来就行,记住自己安装路径,安装完后,将目录添加到系统环境变量path就可以了。 Pycharm官网 https://www.jetbrains.com/pycharm
Win10环境下在anaconda安装tensorflow2.1
Win10环境下在anaconda安装tensorflow2.1前言正文一、安装预知二、安装流程1.安装Anaconda2.安装Tensorflow3.测试及预期结果后记 前言 2020年2月18日尝试在电脑上安装tensorflow用于完成本科毕设。参考了两篇博客(其实是完全依赖[狗头]),受益匪浅,链接如下: 1.博客:https://blog.csdn.net/zhanghai4155/article/details/104268737; 2.博客:https://www.cnblogs.com/ming-4/p/11516728.html; 安装的时候是照着博客1安装的,后来按照其测试
tensorflow1.12支持cuda10
自编译tensorflow: 1.python3.5,tensorflow1.12; 2.支持cuda10.0,cudnn7.3.1,TensorRT-5.0.2.6-cuda10.0-cudnn7.3; 3.无mkl支持; 软硬件硬件环境:Ubuntu16.04,GeForce GTX 1080 TI 配置信息: hp@dla:~/work/ts_compile/tensorflow$ ./configure WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown". You have bazel 0.19.1 installed. Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3 Found possible Python library paths: /usr/local/lib/python3.5/dist-packages /usr/lib/python3/dist-packages Please input the desired Python library path to use. Default is [/usr/local/lib/python3.5/dist-packages] Do you wish to build TensorFlow with XLA JIT support? [Y/n]: XLA JIT support will be enabled for TensorFlow. Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: No OpenCL SYCL support will be enabled for TensorFlow. Do you wish to build TensorFlow with ROCm support? [y/N]: No ROCm support will be enabled for TensorFlow. Do you wish to build TensorFlow with CUDA support? [y/N]: y CUDA support will be enabled for TensorFlow. Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 10.0]: Please specify the location where CUDA 10.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda-10.0 Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7]: 7.3.1 Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-10.0]: Do you wish to build TensorFlow with TensorRT support? [y/N]: y TensorRT support will be enabled for TensorFlow. Please specify the location where TensorRT is installed. [Default is /usr/lib/x86_64-linux-gnu]://home/hp/bin/TensorRT-5.0.2.6-cuda10.0-cudnn7.3/targets/x86_64-linux-gnu Please specify the locally installed NCCL version you want to use. [Default is to use https://github.com/nvidia/nccl]: Please specify a list of comma-separated Cuda compute capabilities you want to build with. You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 6.1,6.1,6.1]: Do you want to use clang as CUDA compiler? [y/N]: nvcc will be used as CUDA compiler. Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: Do you wish to build TensorFlow with MPI support? [y/N]: No MPI support will be enabled for TensorFlow. Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]: Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: Not configuring the WORKSPACE for Android builds. Preconfigured Bazel build configs. You can use any of the below by adding "--config=" to your build command. See .bazelrc for more details. --config=mkl # Build with MKL support. --config=monolithic # Config for mostly static monolithic build. --config=gdr # Build with GDR support. --config=verbs # Build with libverbs support. --config=ngraph # Build with Intel nGraph support. --config=dynamic_kernels # (Experimental) Build kernels into separate shared objects. Preconfigured Bazel build configs to DISABLE default on features: --config=noaws # Disable AWS S3 filesystem support. --config=nogcp # Disable GCP support. --config=nohdfs # Disable HDFS support. --config=noignite # Disable Apacha Ignite support. --config=nokafka # Disable Apache Kafka support. --config=nonccl # Disable NVIDIA NCCL support. Configuration finished 编译: bazel build --config=opt --verbose_failures //tensorflow/tools/pip_package:build_pip_package 卸载已有tensorflow: hp@dla:~/temp$ sudo pip3 uninstall tensorflow 安装自己编译的成果: hp@dla:~/temp$ sudo pip3 install tensorflow-1.12.0-cp35-cp35m-linux_x86_64.whl
tensorflow-1.15-v10.tar.gz
适用于飞腾CPU(arm64),麒麟V10系统,python3.7.4
tensorflow-r1.10.zip
tensorflow编译C++所需源码 版本r1.10,GitHub下载太慢了,上传资源分享
cifar10_tensorflow_v1.4
cifar10的tensorflow1.4版本,已经将官网代码版本变更的部分改好了,可以直接用
win10安装tensorflow-gpu1.8.0详细完整步骤
主要介绍了win10安装tensorflow-gpu1.8.0详细完整步骤,本文给大家介绍的非常详细,具有一定的参考借鉴价值,需要的朋友可以参考下
Visual Studio 2019下配置 CUDA 10.1 + TensorFlow-GPU 1.14.0
主要介绍了Visual Studio 2019下配置 CUDA 10.1 + TensorFlow-GPU 1.14.0,文中通过示例代码介绍的非常详细,对大家的学习或者工作具有一定的参考学习价值,需要的朋友们下面随着小编来一起学习学习吧
Ananconda3 + Tensorflow-2.1.0 + Tensorflow-1.14.0 的安装配置(win10)
目录 下载 Anaconda Anaconda 安装 + 环境配置 配置 Anaconda 环境变量 检验:安装成功 新增Anaconda 中国镜像 配置环境 最新的 tensorflow-2.1.0 创建新环境 tensorflow2 新环境下安装 tensorflow-2.1.0 退出环境 测试:tensorflow-2.1.0 安装成功 安装 tensorflow 1.14.0 tensorflow-1.14.0 版本安装成功 常用的 conda 命令 参考 下载 Anaconda 系统环境:win10, 64位。 Pycharm 2019.3.3 任务:安装Anaconda+配置环
tensorflow-1.13.1-win10-cuda10-VS2015-c++dev.rar
tensorflow1.3_cuda10_vs2015版本,windows 下编译tf1.3 gup 版本,包含bin lib ,include 文件
Tensorflow-1.10.1-源码
Tensorflow-1.10.1-源码 Tensorflow-1.10.1-源码 Tensorflow-1.10.1-源码 Tensorflow-1.10.1-源码 Tensorflow-1.10.1-源码 Tensorflow-1.10.1-源码 Tensorflow-1.10.1-源码 Tensorflow-1.10.1-源码 Tensorflow-1.10.1-源码 Tensorflow-1.10.1-源码
tensorflow1.13.1_win_cuda10.0+cudnn7.6.3.rar
windows10下VS2015编译的tensorflow1.13.1的C接口库,cuda10.0+cudnn7.6.3
最新推荐





