在 windows anaconda 中安装 tensorflow - 并 运行 使用 Spyder GUI 使用它
installing tensorflow in windows anaconda - and running using it using Spyder GUI
我访问了 the tensorflow page 并遵循了 Installing with Anaconda 部分的说明。当我尝试验证我的安装时，出现以下错误
(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()
然后我尝试了
(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\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\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\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\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\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\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\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\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：
- 我是否应该在输入命令后验证安装 -
activate tensorflow如上面第二个命令方块所示？
- 为什么我在之后会收到多个指令
命令
sess = tf.Session() ?
我可以在
间谍桂？如何？我在下面尝试过，但在 SPYDER gui 中，但没有取得任何成功:(
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，您在虚拟环境中安装了tensorflow，需要激活虚拟环境才能导入tensorflow。
Q2：不知道为什么会有多条指令，但这是正常的，是tensorflow内置的。您可以通过启用 SIMD 指令自行构建 tensorflow 来避免这些情况。 https://www.youtube.com/watch?v=ghv5fbC287o
Q3：创建虚拟环境需要修改第一步。使用以下命令创建虚拟环境 {conda create -n tensorflow python=3.5 anaconda}。
您Q3的详细回答如下：
使用"conda create -n tensorflow python=3.5 anaconda"
创建虚拟环境后输入命令"activate tensorflow"
现在使用 "pip install tensorflow"（CPU-only）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，则转到 "C:\ProgramData\Anaconda3\envs\test\Scripts" 并打开 spyder.exe。你应该可以在这个环境中成功导入tensorflow。
您应该从命令提示符激活您的虚拟环境。激活后，您应该 运行 命令 spyder 将从您的虚拟环境
打开 spyder gui
问题是你的tensorflow是安装在conda环境下的。所以首先以管理员身份打开 conda 提示符，然后输入 'activate tensorflow' 激活 tensorflow 环境，然后输入 spyder 打开你的 spyder gui。它主要会解决问题。
我访问了 the tensorflow page 并遵循了 Installing with Anaconda 部分的说明。当我尝试验证我的安装时，出现以下错误
(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()
然后我尝试了
(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\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\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\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\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\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\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\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\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：
- 我是否应该在输入命令后验证安装 -
activate tensorflow如上面第二个命令方块所示？ - 为什么我在之后会收到多个指令
命令
sess = tf.Session()? 我可以在 间谍桂？如何？我在下面尝试过，但在 SPYDER gui 中，但没有取得任何成功:(
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，您在虚拟环境中安装了tensorflow，需要激活虚拟环境才能导入tensorflow。
Q2：不知道为什么会有多条指令，但这是正常的，是tensorflow内置的。您可以通过启用 SIMD 指令自行构建 tensorflow 来避免这些情况。 https://www.youtube.com/watch?v=ghv5fbC287o
Q3：创建虚拟环境需要修改第一步。使用以下命令创建虚拟环境 {conda create -n tensorflow python=3.5 anaconda}。
您Q3的详细回答如下：
使用"conda create -n tensorflow python=3.5 anaconda"
创建tensorflow环境
创建虚拟环境后输入命令"activate tensorflow"
现在使用 "pip install tensorflow"（CPU-only）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，则转到 "C:\ProgramData\Anaconda3\envs\test\Scripts" 并打开 spyder.exe。你应该可以在这个环境中成功导入tensorflow。
您应该从命令提示符激活您的虚拟环境。激活后，您应该 运行 命令 spyder 将从您的虚拟环境
问题是你的tensorflow是安装在conda环境下的。所以首先以管理员身份打开 conda 提示符，然后输入 'activate tensorflow' 激活 tensorflow 环境，然后输入 spyder 打开你的 spyder gui。它主要会解决问题。