我访问了tensorflow页面 ，并按照“ Installing with Anaconda一节中的说明进行操作。 当我试图validation我的安装，我得到了以下错误
(C:\ProgramData\Anaconda3) C:\Users\nik>python Python 3.6.1 |Anaconda 4.4.0 (64-bit)| (default, May 11 2017, 13:25:24) [MSC v.1900 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf Traceback (most recent call last): File "<stdin>", line 1, in <module> ModuleNotFoundError: No module named 'tensorflow' >>> hello = tf.constant('Hello, TensorFlow!') Traceback (most recent call last): File "<stdin>", line 1, in <module> NameError: name 'tf' is not defined >>> exit Use exit() or Ctrl-Z plus Return to exit >>> exit()
然后我试了
(C:\ProgramData\Anaconda3) C:\Users\nik>activate tensorflow (tensorflow) C:\Users\nik>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.1-cp35-cp35m-win_amd64.whl Collecting tensorflow==1.2.1 from https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.1-cp35-cp35m-win_amd64.whl Using cached https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.1-cp35-cp35m-win_amd64.whl Collecting bleach==1.5.0 (from tensorflow==1.2.1) Using cached bleach-1.5.0-py2.py3-none-any.whl Collecting html5lib==0.9999999 (from tensorflow==1.2.1) Collecting backports.weakref==1.0rc1 (from tensorflow==1.2.1) Using cached backports.weakref-1.0rc1-py3-none-any.whl Collecting werkzeug>=0.11.10 (from tensorflow==1.2.1) Using cached Werkzeug-0.12.2-py2.py3-none-any.whl Collecting markdown>=2.6.8 (from tensorflow==1.2.1) Collecting protobuf>=3.2.0 (from tensorflow==1.2.1) Collecting numpy>=1.11.0 (from tensorflow==1.2.1) Using cached numpy-1.13.1-cp35-none-win_amd64.whl Collecting six>=1.10.0 (from tensorflow==1.2.1) Using cached six-1.10.0-py2.py3-none-any.whl Collecting wheel>=0.26 (from tensorflow==1.2.1) Using cached wheel-0.29.0-py2.py3-none-any.whl Collecting setuptools (from protobuf>=3.2.0->tensorflow==1.2.1) Using cached setuptools-36.2.0-py2.py3-none-any.whl Installing collected packages: six, html5lib, bleach, backports.weakref, werkzeug, markdown, setuptools, protobuf, numpy, wheel, tensorflow Successfully installed backports.weakref-1.0rc1 bleach-1.5.0 html5lib-0.9999999 markdown-2.6.8 numpy-1.13.1 protobuf-3.3.0 setuptools-36.2.0 six-1.10.0 tensorflow-1.2.1 werkzeug-0.12.2 wheel-0.29.0 (tensorflow) C:\Users\nik>python Python 3.5.3 |Continuum Analytics, Inc.| (default, May 15 2017, 10:43:23) [MSC v.1900 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf >>> hello = tf.constant('Hello, TensorFlow!') >>> sess = tf.Session() 2017-07-20 12:20:26.177654: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations. 2017-07-20 12:20:26.178276: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations. 2017-07-20 12:20:26.178687: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. 2017-07-20 12:20:26.179189: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 2017-07-20 12:20:26.179713: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 2017-07-20 12:20:26.180250: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 2017-07-20 12:20:26.180687: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 2017-07-20 12:20:26.181092: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. >>> print(sess.run(hello)) b'Hello, TensorFlow!'
我的问题如下 – 我的主要问题是问题3：
activate tensorflow ，如上面的第二个命令块所示？ sess = tf.Session() ？ 我可以在SPYDER GUI中使用tensorflow吗？ 怎么样？ 我试过下面，但在SPYDER GUI，但没有得到任何成功:(
激活tensorflow
文件“”，第1行
activate tensorflow ^ SyntaxError: invalid syntax import tensorflow as tf Traceback (most recent call last): File "<ipython-input-2-41389fad42b5>", line 1, in <module> import tensorflow as tf ModuleNotFoundError: No module named 'tensorflow'
Q1 ：是的，您需要激活虚拟环境才能导入张量流，因为您已经在虚拟环境中安装了tensorflow。
问题2 ：不知道为什么有多条指令，但是这是正常的，并且内置在张量流中。 您可以通过在启用SIMD指令的情况下自行构建tensorflow来避免这些问题。 https://www.youtube.com/watch?v=ghv5fbC287o
问题3 ：创建虚拟环境时需要更改第一步。 使用以下命令创建虚拟环境{conda create -n tensorflow python = 3.5 anaconda}。
第三季度的详细答案如下：
使用“conda create -n tensorflow python = 3.5 anaconda”创建tensorflow环境
一旦创建虚拟环境，请输入命令“激活tensorflow”
现在使用“pip install tensorflow”（仅限CPU）或pip install tensorflow-gpu（对于GPU）安装tensorflow。
现在转到安装anaconda的文件夹。
如果C：\ ProgramData \ Anaconda3是Anaconda根文件夹，则进入“C：\ ProgramData \ Anaconda3 \ envs \ test \ Scripts”并打开spyder.exe。 您应该能够在此环境中成功导入tensorflow。
您应该从命令提示符处激活您的虚拟环境。 一旦激活，你应该运行命令spyder ，这将打开虚拟环境中的spyder gui