在Windows anaconda中安装tensorflow - 并使用Spyder GUI运行它

问题描述 投票：1回答：3

我访问了the tensorflow page并遵循了Installing with Anaconda部分的指示。当我尝试验证我的安装时，我遇到了以下错误

(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()

然后我试过了

(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：

  1. 我想在键入命令 - activate tensorflow之后验证安装，如上面的第二个命令块所示？
  2. 为什么我在命令sess = tf.Session()后得到多个指令？
  3. 我可以在SPYDER gui中使用tensorflow吗？怎么样？我试过下面但是在SPYDER gui，但没有取得任何成功:( 激活张量流

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'
python windows tensorflow anaconda spyder
3个回答
2
投票

Q1：是的，您需要激活虚拟环境以导入tensorflow，因为您已在虚拟环境中安装了tensorflow。

Q2：不确定为什么有多个指令但是这是正常的并且内置在tensorflow中。您可以通过在启用SIMD指令的情况下自行构建张量流来避免这些问题。 https://www.youtube.com/watch?v=ghv5fbC287o

问题3：您需要在创建虚拟环境时更改第一步。使用以下命令创建虚拟环境{conda create -n tensorflow python = 3.5 anaconda}。

Q3的详细答案如下：

  1. 使用“conda create -n tensorflow python = 3.5 anaconda”创建tensorflow环境
  2. 创建虚拟环境后，输入命令“activate tensorflow”
  3. 现在使用“pip install tensorflow”（仅限CPU）或pip install tensorflow-gpu（用于GPU）安装tensorflow。
  4. 现在转到安装anaconda的文件夹。
  5. 如果C：\ ProgramData \ Anaconda3是Anaconda根文件夹，则转到“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。
0
投票

您应该从命令提示符处激活虚拟环境。一旦激活，你应该运行命令spyderwhich将打开你的虚拟环境中的spyder gui

0
投票

问题是你的张量流安装在conda环境中。因此，首先以管理员身份打开conda提示符，然后键入“activate tensorflow”激活tensorflow环境，然后键入spyder打开你的spyder gui。它主要解决问题。

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