win10+Anaconda+Tensorflow1.3+CUDA8.0+python3.5+pycharm+opencv3
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1.准备安装包
Anaconda3-4.2.0-Windows-x86_64;
pycharm-community;
CUDA:cuda_8.0.61_win10;下载时选择 exe(local)
cuDNN:cudnn-8.0-windows10-x64-v6.0;如果你安装的TensorFlow版本和我一样1.3,请下载cuDNN v6.0 for CUDA 8.0
2.安装CUDA8.0
装完后在cmd里查看版本号: nvcc -V
3.安装cuDNN库
解压后将文件分别放在 v8.0 对应的目录下
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0
4.安装Anaconda
我是选择用Anaconda安装TensorFlow,方便管理各种环境。
下载对应安装包,按提示安装。
装好后,打开Anaconda Prompt.添加清华的镜像源
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/conda config --set show_channel_urls yes
在win下更改Python的默认源为清华源:
在当前的用户目录下新建pip文件夹,在pip文件夹里新建pip.ini文件:
[global] index-url = http://mirrors.aliyun.com/pypi/simple///https://pypi.tuna.tsinghua.edu.cn/simple 清华源[install] trusted-host=mirrors.aliyun.com
在Anaconda里建立TensorFlow的环境:
conda create -n tensorflow Python=3.5
激活TensorFlow环境:
activate TensorFlow
关闭环境:
deactivate
5.安装TensorFlow
pip install --upgrade --ignore-installed tensorflow-gpu
安装TensorFlow指定版本(清华源上有的,更换链接最后的版本名称就行了)
pip install --upgrade https://mirrors.tuna.tsinghua.edu.cn/tensorflow/windows/gpu/tensorflow_gpu-1.3.0rc0-cp35-cp35m-win_amd64.whl
简单测试:
在TensorFlow环境里打开Python:
import tensorflow as tfhello = tf.constant("Hello!TensorFlow")sess = tf.Session()print(sess.run(hello))
见到b’hello tensorflow’ 测试成功。
6.安装,配置pycharm
下载对应安装包。按提示安装pycharm。
配置pycharm:
在选择Python版本时,手动添加位于Anaconda的python版本,位置在Anaconda的安装目录下的envs文件夹里。
7.在pycharm里新建mnist手写识别程序:
from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data/",one_hot=True)import tensorflow as tfsess = tf.InteractiveSession()x = tf.placeholder(tf.float32,[None,784])w = tf.Variable(tf.zeros([784,10]))b = tf.Variable(tf.zeros([10]))y = tf.nn.softmax(tf.matmul(x,w) + b)y_ = tf.placeholder(tf.float32,[None,10])cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y),reduction_indices=[1]))train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)tf.global_variables_initializer().run()for i in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) train_step.run({x: batch_xs, y_:batch_ys})correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels}))
8.配置opencv3
在cmd中键入
conda install --channel https://conda.anaconda.org/menpo opencv3
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