入坑tensorflow
win10 CPU版，anaconda prompt命令行一句话，pip install --upgrade tensorflow搞定。比caffe好装一万倍。
gpu版没装成，首先这个笔记本没装cuda,另外一个台式装好了cuda8.0和cunn5.1也是报一样的错误，缺少一个setuptool.egg
命令行如下：
(D:\Users\song\Anaconda3) C:\SPB_Data>python --version
Python 3.6.0 :: Anaconda 4.3.1 (64-bit) (D:\Users\song\Anaconda3) C:\SPB_Data>pip -V
pip 9.0.1 from D:\Users\song\Anaconda3\lib\site-packages (python 3.6) (D:\Users\song\Anaconda3) C:\SPB_Data>pip3 install --upgrade tensorflow-gpu
'pip3' 不是内部或外部命令，也不是可运行的程序
或批处理文件。 (D:\Users\song\Anaconda3) C:\SPB_Data>pip install --upgrade tensorflow-gpu
Collecting tensorflow-gpu
Downloading tensorflow_gpu-1.3.0-cp36-cp36m-win_amd64.whl (60.0MB)
100% |████████████████████████████████| 60.0MB 16kB/s
Collecting numpy>=1.11.0 (from tensorflow-gpu)
Downloading numpy-1.13.3-cp36-none-win_amd64.whl (13.1MB)
100% |████████████████████████████████| 13.1MB 73kB/s
Collecting protobuf>=3.3.0 (from tensorflow-gpu)
Downloading protobuf-3.4.0-py2.py3-none-any.whl (375kB)
100% |████████████████████████████████| 378kB 667kB/s
Collecting wheel>=0.26 (from tensorflow-gpu)
Downloading wheel-0.30.0-py2.py3-none-any.whl (49kB)
100% |████████████████████████████████| 51kB 371kB/s
Collecting tensorflow-tensorboard<.2.0,>=0.1.0 (from tensorflow-gpu)
Downloading tensorflow_tensorboard-0.1.8-py3-none-any.whl (1.6MB)
100% |████████████████████████████████| 1.6MB 413kB/s
Collecting six>=1.10.0 (from tensorflow-gpu)
Downloading six-1.11.0-py2.py3-none-any.whl
Collecting setuptools (from protobuf>=3.3.0->tensorflow-gpu)
Downloading setuptools-36.6.0-py2.py3-none-any.whl (481kB)
100% |████████████████████████████████| 481kB 734kB/s
Collecting bleach==1.5.0 (from tensorflow-tensorboard<.2.0,>=0.1.0->tensorflow-gpu)
Downloading bleach-1.5.0-py2.py3-none-any.whl
Collecting werkzeug>=0.11.10 (from tensorflow-tensorboard<.2.0,>=0.1.0->tensorflow-gpu)
Downloading Werkzeug-0.12.2-py2.py3-none-any.whl (312kB)
100% |████████████████████████████████| 317kB 1.7MB/s
Collecting html5lib==0.9999999 (from tensorflow-tensorboard<.2.0,>=0.1.0->tensorflow-gpu)
Downloading html5lib-0.9999999.tar.gz (889kB)
100% |████████████████████████████████| 890kB 502kB/s
Collecting markdown>=2.6.8 (from tensorflow-tensorboard<.2.0,>=0.1.0->tensorflow-gpu)
Downloading Markdown-2.6.9.tar.gz (271kB)
100% |████████████████████████████████| 276kB 687kB/s
Building wheels for collected packages: html5lib, markdown
Running setup.py bdist_wheel for html5lib ... done
Stored in directory: C:\Users\song\AppData\Local\pip\Cache\wheels\6f\85\6c\56b8e1292c6214c4eb73b9dda50f53e8e977bf65989373c962
Running setup.py bdist_wheel for markdown ... done
Stored in directory: C:\Users\song\AppData\Local\pip\Cache\wheels\bf\46\10\c93e17ae86ae3b3a919c7b39dad3b5ccf09aeb066419e5c1e5
Successfully built html5lib markdown
Installing collected packages: numpy, setuptools, six, protobuf, wheel, html5lib, bleach, werkzeug, markdown, tensorflow-tensorboard, tensorflow-gpu
Found existing installation: numpy 1.11.3
Uninstalling numpy-1.11.3:
Successfully uninstalled numpy-1.11.3
Found existing installation: setuptools 27.2.0
Uninstalling setuptools-27.2.0:
Successfully uninstalled setuptools-27.2.0
Found existing installation: six 1.10.0
DEPRECATION: Uninstalling a distutils installed project (six) has been deprecated and will be removed in a future version. This is due to the fact that uninstalling a distutils project will only partially uninstall the project.
Uninstalling six-1.10.0:
Successfully uninstalled six-1.10.0
Found existing installation: wheel 0.29.0
Uninstalling wheel-0.29.0:
Successfully uninstalled wheel-0.29.0
Found existing installation: Werkzeug 0.11.15
Uninstalling Werkzeug-0.11.15:
Successfully uninstalled Werkzeug-0.11.15
Successfully installed bleach-1.5.0 html5lib-0.9999999 markdown-2.6.9 numpy-1.13.3 protobuf-3.4.0 setuptools-36.6.0 six-1.11.0 tensorflow-gpu-1.3.0 tensorflow-tensorboard-0.1.8 werkzeug-0.12.2 wheel-0.30.0
Traceback (most recent call last):
File "D:\Users\song\Anaconda3\Scripts\pip-script.py", line 5, in <module>
sys.exit(pip.main())
File "D:\Users\song\Anaconda3\lib\site-packages\pip\__init__.py", line 249, in main
return command.main(cmd_args)
File "D:\Users\song\Anaconda3\lib\site-packages\pip\basecommand.py", line 252, in main
pip_version_check(session)
File "D:\Users\song\Anaconda3\lib\site-packages\pip\utils\outdated.py", line 102, in pip_version_check
installed_version = get_installed_version("pip")
File "D:\Users\song\Anaconda3\lib\site-packages\pip\utils\__init__.py", line 838, in get_installed_version
working_set = pkg_resources.WorkingSet()
File "D:\Users\song\Anaconda3\lib\site-packages\pip\_vendor\pkg_resources\__init__.py", line 644, in __init__
self.add_entry(entry)
File "D:\Users\song\Anaconda3\lib\site-packages\pip\_vendor\pkg_resources\__init__.py", line 700, in add_entry
for dist in find_distributions(entry, True):
File "D:\Users\song\Anaconda3\lib\site-packages\pip\_vendor\pkg_resources\__init__.py", line 1949, in find_eggs_in_zip
if metadata.has_metadata('PKG-INFO'):
File "D:\Users\song\Anaconda3\lib\site-packages\pip\_vendor\pkg_resources\__init__.py", line 1463, in has_metadata
return self.egg_info and self._has(self._fn(self.egg_info, name))
File "D:\Users\song\Anaconda3\lib\site-packages\pip\_vendor\pkg_resources\__init__.py", line 1823, in _has
return zip_path in self.zipinfo or zip_path in self._index()
File "D:\Users\song\Anaconda3\lib\site-packages\pip\_vendor\pkg_resources\__init__.py", line 1703, in zipinfo
return self._zip_manifests.load(self.loader.archive)
File "D:\Users\song\Anaconda3\lib\site-packages\pip\_vendor\pkg_resources\__init__.py", line 1643, in load
mtime = os.stat(path).st_mtime
FileNotFoundError: [WinError 2] 系统找不到指定的文件。: 'D:\\Users\\song\\Anaconda3\\lib\\site-packages\\setuptools-27.2.0-py3.6.egg' (D:\Users\song\Anaconda3) C:\SPB_Data>cd .. (D:\Users\song\Anaconda3) C:\>cd .. (D:\Users\song\Anaconda3) C:\>ls
'ls' 不是内部或外部命令，也不是可运行的程序
或批处理文件。 (D:\Users\song\Anaconda3) C:\>python --version
Python 3.6.0 :: Anaconda 4.3.1 (64-bit) (D:\Users\song\Anaconda3) C:\>nvcc -V
'nvcc' 不是内部或外部命令，也不是可运行的程序
或批处理文件。 (D:\Users\song\Anaconda3) C:\>pip install --upgrade tensorflow
Collecting tensorflow
Downloading tensorflow-1.3.0-cp36-cp36m-win_amd64.whl (25.5MB)
100% |████████████████████████████████| 25.5MB 29kB/s
Requirement already up-to-date: protobuf>=3.3.0 in d:\users\song\anaconda3\lib\site-packages (from tensorflow)
Requirement already up-to-date: wheel>=0.26 in d:\users\song\anaconda3\lib\site-packages (from tensorflow)
Requirement already up-to-date: tensorflow-tensorboard<.2.0,>=0.1.0 in d:\users\song\anaconda3\lib\site-packages (from tensorflow)
Requirement already up-to-date: six>=1.10.0 in d:\users\song\anaconda3\lib\site-packages (from tensorflow)
Requirement already up-to-date: numpy>=1.11.0 in d:\users\song\anaconda3\lib\site-packages (from tensorflow)
Requirement already up-to-date: setuptools in d:\users\song\anaconda3\lib\site-packages (from protobuf>=3.3.0->tensorflow)
Requirement already up-to-date: markdown>=2.6.8 in d:\users\song\anaconda3\lib\site-packages (from tensorflow-tensorboard<.2.0,>=0.1.0->tensorflow)
Requirement already up-to-date: bleach==1.5.0 in d:\users\song\anaconda3\lib\site-packages (from tensorflow-tensorboard<.2.0,>=0.1.0->tensorflow)
Requirement already up-to-date: html5lib==0.9999999 in d:\users\song\anaconda3\lib\site-packages (from tensorflow-tensorboard<.2.0,>=0.1.0->tensorflow)
Requirement already up-to-date: werkzeug>=0.11.10 in d:\users\song\anaconda3\lib\site-packages (from tensorflow-tensorboard<.2.0,>=0.1.0->tensorflow)
Installing collected packages: tensorflow
Successfully installed tensorflow-1.3.0 (D:\Users\song\Anaconda3) C:\>import tensorflow as tf
'import' 不是内部或外部命令，也不是可运行的程序
或批处理文件。 (D:\Users\song\Anaconda3) C:\>python
Python 3.6.0 |Anaconda 4.3.1 (64-bit)| (default, Dec 23 2016, 11:57:41) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> a = tf.random_normal((100,100))
>>> b = tf.random_normal((100,500))
>>> c=tf.matmul(a,b)
>>> sess=tf.InteractiveSession()
2017-10-29 20:46:03.615036: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\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.
2017-10-29 20:46:03.620666: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\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.
>>> sess.run(c)
array([[ -2.01546478e+01, -1.21840429e+01, 8.52634966e-01, ...,
-1.93460350e+01, -1.17136412e+01, -2.81856956e+01],
[ -2.86180496e+00, 1.86777287e+01, 2.39728212e-01, ...,
1.65606441e+01, -8.35585117e+00, 1.21092701e+01],
[ -6.70668936e+00, -1.92020512e+00, -8.63678837e+00, ...,
1.19851971e+01, -1.95774388e+00, -3.46706104e+00],
...,
[ -6.20419502e+00, -1.58898029e+01, 1.47155542e+01, ...,
-6.35781908e+00, -7.09256840e+00, 1.04180880e+01],
[ -1.14867371e-03, -2.47349381e+00, 1.40450490e+00, ...,
1.87805653e+00, 7.70393276e+00, -1.11452806e+00],
[ -1.81114292e+01, 2.83652916e+01, 2.23067703e+01, ...,
4.72095060e+00, 2.01743245e+00, 9.46466255e+00]], dtype=float32)
>>> c
<tf.Tensor 'MatMul:0' shape=(100, 500) dtype=float32>
>>> print(c)
Tensor("MatMul:0", shape=(100, 500), dtype=float32)
>>> print(c.val)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'Tensor' object has no attribute 'val'
>>> print(c.eval())
[[ 7.44645548e+00 7.01777339e-01 -3.29522681e+00 ..., -4.11035490e+00
6.88585615e+00 -1.03243275e+01]
[ 1.74935007e+00 -8.06512642e+00 -8.94767094e+00 ..., -8.51691341e+00
-6.86603403e+00 9.46757889e+00]
[ -6.61030436e+00 5.86357307e+00 1.51259956e+01 ..., -9.53737926e+00
1.95381641e-02 1.16717541e+00]
...,
[ -5.34449625e+00 1.13798809e+00 1.34737101e+01 ..., 6.86746025e+00
3.37234330e+00 -9.16017354e-01]
[ -3.89829564e+00 1.19947767e+00 9.16424465e+00 ..., 7.61591375e-01
-1.70225441e-01 1.02892227e+01]
[ 1.97680518e-01 -1.99925423e+01 -9.40755844e+00 ..., 5.44214249e+00
1.52138865e+00 2.48984170e+00]]
>>> print(a)
Tensor("random_normal:0", shape=(100, 100), dtype=float32)
>>> sess=tf.InteractiveSession()
>>> print(sess.run(a))
[[-1.394485 -1.95048952 0.76553309 ..., -0.43924141 -1.21975422
0.60572529]
[ 0.34292024 0.86016667 -2.25437665 ..., 1.67957187 1.57846153
-1.53106809]
[ 0.08453497 0.59995687 -1.37805259 ..., -0.92989731 -0.07856822
-1.36062932]
...,
[-0.41187105 0.60689414 -0.44695681 ..., 0.51408201 -1.49676847
0.95741159]
[-1.01903558 -1.24220276 0.12283699 ..., 0.53144586 -0.2782338
0.34964591]
[ 0.27783027 0.5017578 -1.0619179 ..., 0.4974283 -0.04771407
0.48028085]]
>>> ls
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'ls' is not defined
>>> exit() (D:\Users\song\Anaconda3) C:\>e:\
'e:\' 不是内部或外部命令，也不是可运行的程序
或批处理文件。 (D:\Users\song\Anaconda3) C:\>cd e:\ (D:\Users\song\Anaconda3) C:\>python minst.py
File "minst.py", line 16
SyntaxError: Non-UTF-8 code starting with '\xb0' in file minst.py on line 16, but no encoding declared; see http://python.org/dev/peps/pep-0263/ for details (D:\Users\song\Anaconda3) C:\>python minst.py
File "minst.py", line 16
SyntaxError: Non-UTF-8 code starting with '\xb0' in file minst.py on line 16, but no encoding declared; see http://python.org/dev/peps/pep-0263/ for details (D:\Users\song\Anaconda3) C:\>python minst.py
File "minst.py", line 16
SyntaxError: Non-UTF-8 code starting with '\xb0' in file minst.py on line 16, but no encoding declared; see http://python.org/dev/peps/pep-0263/ for details (D:\Users\song\Anaconda3) C:\>python
Python 3.6.0 |Anaconda 4.3.1 (64-bit)| (default, Dec 23 2016, 11:57:41) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> flags = tf.app.flags
>>> FLAGS = flags.FLAGS
>>> flags.DEFINE_string('data_dir', '/tmp/data/', 'Directory for storing data')
>>>
>>> mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'input_data' is not defined
>>> from __future__ import absolute_import
>>> from __future__ import division
>>> from __future__ import print_function
>>> from tensorflow.examples.tutorials.mnist import input_data
>>>
>>> mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting /tmp/data/train-images-idx3-ubyte.gz
Traceback (most recent call last):
File "D:\Users\song\Anaconda3\lib\urllib\request.py", line 1318, in do_open
encode_chunked=req.has_header('Transfer-encoding'))
File "D:\Users\song\Anaconda3\lib\http\client.py", line 1239, in request
self._send_request(method, url, body, headers, encode_chunked)
File "D:\Users\song\Anaconda3\lib\http\client.py", line 1285, in _send_request
self.endheaders(body, encode_chunked=encode_chunked)
File "D:\Users\song\Anaconda3\lib\http\client.py", line 1234, in endheaders
self._send_output(message_body, encode_chunked=encode_chunked)
File "D:\Users\song\Anaconda3\lib\http\client.py", line 1026, in _send_output
self.send(msg)
File "D:\Users\song\Anaconda3\lib\http\client.py", line 964, in send
self.connect()
File "D:\Users\song\Anaconda3\lib\http\client.py", line 1400, in connect
server_hostname=server_hostname)
File "D:\Users\song\Anaconda3\lib\ssl.py", line 401, in wrap_socket
_context=self, _session=session)
File "D:\Users\song\Anaconda3\lib\ssl.py", line 808, in __init__
self.do_handshake()
File "D:\Users\song\Anaconda3\lib\ssl.py", line 1061, in do_handshake
self._sslobj.do_handshake()
File "D:\Users\song\Anaconda3\lib\ssl.py", line 683, in do_handshake
self._sslobj.do_handshake()
ssl.SSLEOFError: EOF occurred in violation of protocol (_ssl.c:749) During handling of the above exception, another exception occurred: Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\Users\song\Anaconda3\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py", line 240, in read_data_sets
SOURCE_URL + TRAIN_LABELS)
File "D:\Users\song\Anaconda3\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\base.py", line 208, in maybe_download
temp_file_name, _ = urlretrieve_with_retry(source_url)
File "D:\Users\song\Anaconda3\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\base.py", line 165, in wrapped_fn
return fn(*args, **kwargs)
File "D:\Users\song\Anaconda3\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\base.py", line 190, in urlretrieve_with_retry
return urllib.request.urlretrieve(url, filename)
File "D:\Users\song\Anaconda3\lib\urllib\request.py", line 248, in urlretrieve
with contextlib.closing(urlopen(url, data)) as fp:
File "D:\Users\song\Anaconda3\lib\urllib\request.py", line 223, in urlopen
return opener.open(url, data, timeout)
File "D:\Users\song\Anaconda3\lib\urllib\request.py", line 526, in open
response = self._open(req, data)
File "D:\Users\song\Anaconda3\lib\urllib\request.py", line 544, in _open
'_open', req)
File "D:\Users\song\Anaconda3\lib\urllib\request.py", line 504, in _call_chain
result = func(*args)
File "D:\Users\song\Anaconda3\lib\urllib\request.py", line 1361, in https_open
context=self._context, check_hostname=self._check_hostname)
File "D:\Users\song\Anaconda3\lib\urllib\request.py", line 1320, in do_open
raise URLError(err)
urllib.error.URLError: <urlopen error EOF occurred in violation of protocol (_ssl.c:749)>
>>> import requests
>>> >>> from requests.adapters import HTTPAdapter
File "<stdin>", line 1
>>> from requests.adapters import HTTPAdapter
^
SyntaxError: invalid syntax
>>> >>> from requests.packages.urllib3.poolmanager import PoolManager
File "<stdin>", line 1
>>> from requests.packages.urllib3.poolmanager import PoolManager
^
SyntaxError: invalid syntax
>>> >>> import ssl
File "<stdin>", line 1
>>> import ssl
^
SyntaxError: invalid syntax
>>> >>>
File "<stdin>", line 1
>>>
^
SyntaxError: invalid syntax
>>> >>> class MyAdapter(HTTPAdapter):
File "<stdin>", line 1
>>> class MyAdapter(HTTPAdapter):
^
SyntaxError: invalid syntax
>>> ... def init_poolmanager(self, connections, maxsize, block=False):
File "<stdin>", line 1
... def init_poolmanager(self, connections, maxsize, block=False):
^
SyntaxError: invalid syntax
>>> ... self.poolmanager = PoolManager(num_pools=connections,
File "<stdin>", line 1
... self.poolmanager = PoolManager(num_pools=connections,
^
SyntaxError: invalid syntax
>>> ... maxsize=maxsize,
File "<stdin>", line 1
... maxsize=maxsize,
^
SyntaxError: invalid syntax
>>> ... block=block,
File "<stdin>", line 1
... block=block,
^
SyntaxError: invalid syntax
>>> ... ssl_version=ssl.PROTOCOL_TLSv1)
File "<stdin>", line 1
... ssl_version=ssl.PROTOCOL_TLSv1)
^
SyntaxError: invalid syntax
>>> ...
Ellipsis
>>> >>> s = requests.Session()
File "<stdin>", line 1
>>> s = requests.Session()
^
SyntaxError: invalid syntax
>>> >>> s.mount('https://', MyAdapter())
File "<stdin>", line 1
>>> s.mount('https://', MyAdapter())
^
SyntaxError: invalid syntax
>>> >>> s.get('https://www.supercash.cz')
File "<stdin>", line 1
>>> s.get('https://www.supercash.cz')
^
SyntaxError: invalid syntax
>>> <Response [200]>
File "<stdin>", line 1
<Response [200]>
^
SyntaxError: invalid syntax
>>> mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
Extracting /tmp/data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz
>>> x = tf.placeholder(tf.float32, [None, 784]) # 占位符
>>> y = tf.placeholder(tf.float32, [None, 10])
>>> W = tf.Variable(tf.zeros([784, 10]))
>>> b = tf.Variable(tf.zeros([10]))
>>> a = tf.nn.softmax(tf.matmul(x, W) + b)
>>> cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(a), reduction_indices=[1])) # 损失函数为交叉熵
>>> optimizer = tf.train.GradientDescentOptimizer(0.5) # 梯度下降法，学习速率为0.5
>>> train = optimizer.minimize(cross_entropy) # 训练目标：最小化损失函数
>>>
>>> # Test trained model
... correct_prediction = tf.equal(tf.argmax(a, 1), tf.argmax(y, 1))
>>> accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
>>> correct_prediction = tf.equal(tf.argmax(a, 1), tf.argmax(y, 1))
>>> accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
>>> sess = tf.InteractiveSession() # 建立交互式会话
2017-10-29 21:28:03.960497: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\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.
2017-10-29 21:28:03.968465: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\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.
>>> tf.initialize_all_variables().run()
WARNING:tensorflow:From D:\Users\song\Anaconda3\lib\site-packages\tensorflow\python\util\tf_should_use.py:175: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
>>> for i in range(1000):
... batch_xs, batch_ys = mnist.train.next_batch(100)
... train.run({x: batch_xs, y: batch_ys})
... print(sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}))
File "<stdin>", line 4
print(sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}))
^
SyntaxError: invalid syntax
>>> tf.initialize_all_variables().run()
WARNING:tensorflow:From D:\Users\song\Anaconda3\lib\site-packages\tensorflow\python\util\tf_should_use.py:175: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
>>> tf.global_variables_initializer().run()
>>> for i in range(1000):
... batch_xs, batch_ys = mnist.train.next_batch(100)
... train.run({x: batch_xs, y: batch_ys})
...
>>> print(sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}))
0.9154
>>>
----------2018.02.10 另外一台ubuntu服务器，tensorflow已装好，python能import--------------
python -c 'import tensorflow as tf; print(tf.__version__)'
或：
>>> tf.__version__
>>> tf.__path__
使用TensorFlow-Slim App:
github上models/research/slim目录下的README有详细说明。
python download_and_convert_data.py --dataset_name=flowers --dataset_dir="/home/.../data/"
等一会儿就下载好了，ls /home/.../data可看到：
新建creatingTFSlimDataset.py写入：
import tensorflow as tf
from datasets import flowers slim = tf.contrib.slim # Selects the 'validation' dataset.
dataset = flowers.get_split('validation', "/home/.../data/") # Creates a TF-Slim DataProvider which reads the dataset in the background
# during both training and testing.
provider = slim.dataset_data_provider.DatasetDataProvider(dataset)
[image, label] = provider.get(['image', 'label'])
在slim目录下python creatingTFSlimDataset.py
----------2018.02.11 macbook pro--------------
mac os安装参考官网和教程，每次打开命令行，进入tf目录执行
source bin/activate
进入；执行 deactivate退出tensorflow环境。
----------2018.02.11 用自己的数据集训练cnn----------
代码完全是来自这里，写一个python脚本，把作者的代码贴进去，文件前后分别加上：
import os
import numpy as np
import tensorflow as tf if __name__ == '__main__':
run_training()
这里要么把run_trainning()里的inputData、model等删掉，要么拆成几个.py分别import。
在run_training()里指向自己的数据，作者代码支持2分类，建两个目录，名字分别为0，1，下边直接放图像数据，不能放其他东西。
目录下python xx.py运行，总是报莫名其妙的错误，如下：
2018-02-11 16:14:27.688087: W tensorflow/core/framework/op_kernel.cc:1188] Unimplemented: Cast float to string is not supported
2018-02-11 16:14:27.691410: W tensorflow/core/framework/op_kernel.cc:1188] Unimplemented: Cast float to string is not supported
2018-02-11 16:14:27.700514: W tensorflow/core/framework/op_kernel.cc:1188] Unimplemented: Cast float to string is not supported
2018-02-11 16:14:27.700547: E tensorflow/core/common_runtime/executor.cc:651] Executor failed to create kernel. Unimplemented: Cast float to string is not supported
[[Node: Cast = Cast[DstT=DT_STRING, SrcT=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Cast/x)]]
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected image (JPEG, PNG, or GIF), got unknown format starting with '\000\000\000\001Bud1\000\000(\000\000\000\010\000'
参考这里解决，主要是mac os每个目录下会生成一个.DS_Store隐藏文件，并且每个目录都有，然后get_files里的for in os.listdir(filename+train_class)会把这个不是目录的文件也读进来，用命令：
ls -d .* //显示目录下的隐藏文件
rm .DS_Store //删除
暴力删除所有DS_Store，就可以运行了：
(tf) ...$ python selfDataTest.py
2018-02-11 16:29:32.737708: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.2 AVX AVX2 FMA
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----------2018.02.19 在给定inception v4模型下继续训练---------------
实验记录：
error 1:
InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [5] rhs shape= [1001]
官方给的是1000分类的ImagNet, 自己的数据需要重新训练
error 2:
InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [5] rhs shape= [1001]
因为用cpu跑，修改train_image_classfier.py里的tf.app.Flags.DEFINE_boolean('clone_on_cpu',False,'use CPUs to deploy clones.')，False改成True.
可视化：
调用tensorboard以后显示TensorBoard 1.5.1 at http://localhost:6006 (Press CTRL+C to quit)，在浏览器打开http://localhost:6006即可
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