資訊專欄INFORMATION COLUMN
摘要：而且我們可以看到他自動幫我們安裝了，，等等需要注意的是最后會出現這里選擇才能把加入環境變量中，然后才能使用不然之后就得手動配置。來安裝支持的。步驟中下載太慢了，需要個小時，還是直接在線安裝吧，先下載這個，然后這個只需要分鐘左右。
前言
最近上了幾門深度學習的公開課，還是覺得不過癮，總覺得要搞一個框架來試試。那么caffe,tensorflow,torch等等選哪一個呢？經過一番比較我還是選擇tensorflow,首先他是一個更通用的框架，而且對python支持最好，其次還有google支持，也是開源的，相信在未來無論是學術界還是工業界，他都會流行起來的。
安裝-實況記錄首先得在我的電腦（win10）上裝一個雙系統（不裝虛擬機是因為虛擬機對顯卡等資源的利用不是很好），就裝一個ubuntu吧（版本14.10），怎么裝就不寫了，畢竟網上一大把，然后就是安裝tensorflow了，官網提供了5種安裝辦法，基于pip,基于docker,基于Anaconda，基于Virtualenv，基于源碼。由于Anaconda包含了眾多的科學計算庫，相信對未來的工作能大有用處，所以我就選擇了基于Anaconda的安裝方式。
1.首先在這里選擇相應的Anaconda版本下載。
2.進入下載目錄，輸入命令 bash Anaconda2-4.1.1-Linux-x86_64.sh
然后根據提示進行安裝，他會提示安裝目錄等。而且我們可以看到他自動幫我們安裝了python2.7.12，beautifulsoup，ipython等等：
installing: python-2.7.12-1 ... installing: _nb_ext_conf-0.2.0-py27_0 ... installing: alabaster-0.7.8-py27_0 ... installing: anaconda-client-1.4.0-py27_0 ... installing: anaconda-navigator-1.2.1-py27_0 ... installing: argcomplete-1.0.0-py27_1 ... installing: astropy-1.2.1-np111py27_0 ... installing: babel-2.3.3-py27_0 ... installing: backports-1.0-py27_0 ... installing: backports_abc-0.4-py27_0 ... installing: beautifulsoup4-4.4.1-py27_0 ...
需要注意的是最后會出現：
Do you wish the installer to prepend the Anaconda2 install location to PATH in your /root/.bashrc ? [yes|no]
這里選擇yes才能把anaconda加入環境變量（path）中，然后才能使用,不然之后就得手動配置path。由于修改了環境變量，所以打開一個新的終端來測試安裝結果:在新的終端中輸入python,顯示：
Python 2.7.12 |Anaconda 4.1.1 (64-bit)| (default, Jul 2 2016, 17:42:40) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2 Type "help", "copyright", "credits" or "license" for more information. Anaconda is brought to you by Continuum Analytics. Please check out: http://continuum.io/thanks and https://anaconda.org
可見的確是安裝成功了。
3.conda create -n tensorflow python=2.7 來建立一個conda 計算環境
4.source activate tensorflow 來激活計算環境。
5.pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.8.0rc0-cp27-none-linux_x86_64.whl 來安裝支持GPU的tensorflow。
需要注意，支持GPU要先安裝Cuda Toolkit 和 CUDNN Toolkit（先在官網注冊）
6.安裝成功后打開python,
import tensorflow as tf
然后報了一堆錯：
Traceback (most recent call last): File "", line 1, in File "/root/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/__init__.py", line 23, in from tensorflow.python import * File "/root/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/__init__.py", line 45, in from tensorflow.python import pywrap_tensorflow File "/root/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 28, in _pywrap_tensorflow = swig_import_helper() File "/root/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 24, in swig_import_helper _mod = imp.load_module("_pywrap_tensorflow", fp, pathname, description) ImportError: libcudart.so.7.5: cannot open shared object file: No such file or directory
看樣子是我還沒有安裝好cuda所致。步驟5中下載Cuda Toolkit 太慢了，需要10個小時，還是直接在線安裝吧，先下載這個，然后
dpkg -i cuda-repo-ubuntu1410_7.0-28_amd64.deb apt-get update apt-get install cuda
這個只需要20分鐘左右。安裝好過后cuda應該就在/usr/local/路徑下了。然后安裝CUDNN Toolkit，進入其下載目錄：
tar xvzf cudnn-7.0-linux-x64-v3.0-prod.tgz cp cuda/include/cudnn.h /usr/local/cuda/include cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
然后設置 LD_LIBRARY_PATH 和 CUDA_HOME 環境變量. 可以將下面的命令 添加到 ~/.bashrc文件中, 這樣每次登陸后自動生效：
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64" export CUDA_HOME=/usr/local/cuda
7.測試
測試之時發現依然報上面的錯。libcudart.so.7.5沒找到，我先在磁盤上查找這個文件，locate libcudart.so.7.5，果然沒有，應該是我的cuda版本低了吧，cd /usr/local/cuda/lib64,然后果然發現了libcudart.so.7.0.28，而不是 libcudart.so.7.5
8.重裝Cuda Toolkit
apt-get remove cuda apt-get autoremove #下載http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_7.5-18_amd64.deb apt-get remove cuda-repo-ubuntu1410 dpkg -i cuda-repo-ubuntu1404_7.5-18_amd64.deb#正試圖覆蓋 /etc/apt/sources.list.d/cuda.list，它同時被包含于軟件包 cuda-repo-ubuntu1410 7.0-28,所以必須要上一步 apt-get update sudo apt-get install cuda #報錯：cuda : 依賴: cuda-7-5 (= 7.5-18) 但是它將不會被安裝 #E: 無法修正錯誤，因為您要求某些軟件包保持現狀，就是它們破壞了軟件包間的依賴關系。
太亂了，還是重頭來過吧
同上
同上
conda create -n tensor python=2.7
source activate tensor
安裝Cuda Toolkit，先下載，進入目錄：
dpkg -i cuda-repo-ubuntu1404_7.5-18_amd64.deb apt-get update apt-get install cuda #報錯：cuda : 依賴: cuda-7-5 (= 7.5-18) 但是它將不會被安裝 #E: 無法修正錯誤，因為您要求某些軟件包保持現狀，就是它們破壞了軟件包間的依賴關系。 #也是醉了
裝錯了版本真是麻煩，清理一下系統吧
apt-get --purge remove nvidia-* #徹底卸載nvidia rm -rf anaconda2 # .bashrc文件中刪除關于把anaconda加入環境變量的那一句 #還是不行，依舊報錯：cuda : 依賴: cuda-7-5 (= 7.5-18) 但是它將不會被安裝 #E: 無法修正錯誤，因為您要求某些軟件包保持現狀，就是它們破壞了軟件包間的依賴關系。
搞不定了，還是換成本地安裝試試吧，下載cuda 和 cudnn。奇怪：ubuntu下載很慢，但是windows上就快好多了，在windows上下好直接在ubuntu中拷貝過去吧。
安裝-無bug版 1.由于包依賴問題沒法解決，重裝了系統Ubuntu14.04.5
2.下載cuda 和cudnn，進入下載目錄
dpkg -i cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64.deb sudo apt-get update sudo apt-get install cuda #稍等片刻,然后配置cudnn tar xvzf cudnn-7.5-linux-x64-v5.0-ga-tgz cp cuda/include/cudnn.h /usr/local/cuda/include cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*3.
修改 .bashrc 加入：
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64" export CUDA_HOME=/usr/local/cuda4.
下載Anaconda,進入下載目錄
bash Anaconda2-4.1.1-Linux-x86_64.sh
注意修改配置，根據你的喜好來修改目錄5.
重新打開一個終端
conda create -n tfgpu python=2.7 source activate tfgpu pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl6.
裝好過后，重啟，黑屏了。應該是雙顯卡的問題，不管了，先進入tty試試tensorflow是否裝好了。
Ctrl+Alt+F2#進入tty2,并登陸 root@mageek-ThinkPad-T550:~# source activate tfgpu (tfgpu) root@mageek-ThinkPad-T550:~# python Python 2.7.12 |Continuum Analytics, Inc.| (default, Jul 2 2016, 17:42:40) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2 Type "help", "copyright", "credits" or "license" for more information. Anaconda is brought to you by Continuum Analytics. Please check out: http://continuum.io/thanks and https://anaconda.org >>> import tensorflow as tf I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally >>> sess = tf.Session() I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties: name: GeForce 940M major: 5 minor: 0 memoryClockRate (GHz) 1.124 pciBusID 0000:08:00.0 Total memory: 1023.88MiB Free memory: 997.54MiB I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: Y I tensorflow/core/common_runtime/gpu/gpu_device.cc:839] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce 940M, pci bus id: 0000:08:00.0) >>> (tfgpu) root@mageek-ThinkPad-T550:~# source deactivate
可見是安裝成功了7. 解決黑屏
vim /etc/modprobe.d/blacklist.conf #添加如下幾句來屏蔽一些軟件 blacklist amd76x_edac blacklist vga16fb blacklist nouveau blacklist rivafb blacklist nvidiafb blacklist rivatv #退出 sudo prime-select intel #優先intel集顯 reboot#重啟就進入圖像化界面了8. IPython
這個時候直接用ipython 可以進入界面，但是沒法import tensorflow，要先安裝conda install ipython然后再次進入ipython，就可以了，因為只有執行了這個命令才能將ipython加入虛擬環境tfgpu,在同一個環境中ipython才能找到tensorflow。
9. IDE雖然IPython已經比原生的python終端好多了，但是每次都要敲相同命令，比如import tensorflow as tf還是相當麻煩的，所以還是要搞一個IDE才行。這里推薦Komodo Edit,下載過后，解壓。進入目錄運行 ./install.sh 然后按照提示修改安裝目錄（注意要有權限）。比如我的目錄就是 /usr/local/Komodo-Edit-10/ 然后加入環境變量。這樣就可以重新打開一個終端，命令 komodo，就可以打開這個IDE了，然后配置一些基本的選項比如縮進，配色方案等等就可以正式使用了。
新建一個 tf1.py:
import tensorflow as tf
import numpy as np
# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3
# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but TensorFlow will
# figure that out for us.)
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b
# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
# Before starting, initialize the variables. We will "run" this first.
init = tf.initialize_all_variables()
# Launch the graph.
sess = tf.Session()
sess.run(init)
# Fit the line.
for step in range(201):
sess.run(train)
if step % 20 == 0:
print(step, sess.run(W), sess.run(b))
# Learns best fit is W: [0.1], b: [0.3]
運行：
#進入文件目錄 source activate tfgpu python tf1.py
結果：
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties: name: GeForce 940M major: 5 minor: 0 memoryClockRate (GHz) 1.124 pciBusID 0000:08:00.0 Total memory: 1023.88MiB Free memory: 997.54MiB I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: Y I tensorflow/core/common_runtime/gpu/gpu_device.cc:839] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce 940M, pci bus id: 0000:08:00.0) (0, array([-0.09839484], dtype=float32), array([ 0.5272761], dtype=float32)) (20, array([ 0.02831561], dtype=float32), array([ 0.33592272], dtype=float32)) (40, array([ 0.07941294], dtype=float32), array([ 0.31031665], dtype=float32)) (60, array([ 0.09408762], dtype=float32), array([ 0.30296284], dtype=float32)) (80, array([ 0.09830203], dtype=float32), array([ 0.3008509], dtype=float32)) (100, array([ 0.09951238], dtype=float32), array([ 0.30024436], dtype=float32)) (120, array([ 0.09985995], dtype=float32), array([ 0.3000702], dtype=float32)) (140, array([ 0.09995978], dtype=float32), array([ 0.30002016], dtype=float32)) (160, array([ 0.09998845], dtype=float32), array([ 0.30000579], dtype=float32)) (180, array([ 0.09999669], dtype=float32), array([ 0.30000168], dtype=float32)) (200, array([ 0.09999905], dtype=float32), array([ 0.30000049], dtype=float32))10.NN
#找到tensorflow的目錄 python -c "import os; import inspect; import tensorflow; print(os.path.dirname(inspect.getfile(tensorflow)))" #/root/anaconda2/envs/tfgpu/lib/python2.7/site-packages/tensorflow cd /root/anaconda2/envs/tfgpu/lib/python2.7/site-packages/tensorflow/models/image/mnist/#j進入目錄 python convolutional.py I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally Extracting data/train-images-idx3-ubyte.gz Traceback (most recent call last): File "convolutional.py", line 326, intf.app.run() File "/root/anaconda2/envs/tfgpu/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 30, in run sys.exit(main(sys.argv)) File "convolutional.py", line 138, in main train_data = extract_data(train_data_filename, 60000) File "convolutional.py", line 85, in extract_data buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS) File "/root/anaconda2/envs/tfgpu/lib/python2.7/gzip.py", line 268, in read self._read(readsize) File "/root/anaconda2/envs/tfgpu/lib/python2.7/gzip.py", line 315, in _read self._read_eof() File "/root/anaconda2/envs/tfgpu/lib/python2.7/gzip.py", line 354, in _read_eof hex(self.crc))) IOError: CRC check failed 0x4b01c89e != 0xd2b9b600L
看來是CRC校驗出錯，還是直接去官網下載吧，然后直接拷貝到data路徑中。讀一下convolutional.py就知道下載路徑了，其實比較一下data里程序已經下載的文件和官網的文件就知道程序下載的文件出錯了，文件小了不少，應該是丟包了。
再次執行：
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally Extracting data/train-images-idx3-ubyte.gz Extracting data/train-labels-idx1-ubyte.gz Extracting data/t10k-images-idx3-ubyte.gz Extracting data/t10k-labels-idx1-ubyte.gz I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties: name: GeForce 940M major: 5 minor: 0 memoryClockRate (GHz) 1.124 pciBusID 0000:08:00.0 Total memory: 1023.88MiB Free memory: 997.54MiB I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: Y I tensorflow/core/common_runtime/gpu/gpu_device.cc:839] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce 940M, pci bus id: 0000:08:00.0) Initialized! E tensorflow/stream_executor/cuda/cuda_dnn.cc:347] Loaded runtime CuDNN library: 5005 (compatibility version 5000) but source was compiled with 4007 (compatibility version 4000). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration. F tensorflow/core/kernels/conv_ops.cc:457] Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms) Aborted (core dumped)
意思就是cudnn我安裝的是v5,但是cuda7.5支持的是v4,所以就去下載v4，然后按照步驟2來重新配置cudnnv4:
#這里會覆蓋cudnnv5，所以記得備份cudnnv5,萬一用得上，我把原來解壓的cuda改為cudnn5005 cd /usr/local/cuda/lib64 rm -f libcudnn* #刪掉cudnnv5 #先進入cudnnv4下載目錄 tar xvzf cudnn-7.0-linux-x64-v4.0-prod.tgz cp cuda/include/cudnn.h /usr/local/cuda/include#用v4覆蓋v5 cp cuda/lib64/libcudnn* /usr/local/cuda/lib64#加入v4 chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
再次執行：
cd /root/anaconda2/envs/tfgpu/lib/python2.7/site-packages/tensorflow/models/image/mnist/#j進入目錄 python convolutional.py
結果:
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally Extracting data/train-images-idx3-ubyte.gz Extracting data/train-labels-idx1-ubyte.gz Extracting data/t10k-images-idx3-ubyte.gz Extracting data/t10k-labels-idx1-ubyte.gz E tensorflow/stream_executor/cuda/cuda_driver.cc:491] failed call to cuInit: CUDA_ERROR_NO_DEVICE I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:153] retrieving CUDA diagnostic information for host: mageek-ThinkPad-T550 I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:160] hostname: mageek-ThinkPad-T550 I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:185] libcuda reported version is: 352.63.0 I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:356] driver version file contents: """NVRM version: NVIDIA UNIX x86_64 Kernel Module 352.63 Sat Nov 7 21:25:42 PST 2015 GCC version: gcc version 4.8.4 (Ubuntu 4.8.4-2ubuntu1~14.04.3) """ I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:189] kernel reported version is: 352.63.0 I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:293] kernel version seems to match DSO: 352.63.0 I tensorflow/core/common_runtime/gpu/gpu_init.cc:81] No GPU devices available on machine. Initialized! Step 0 (epoch 0.00), 5.4 ms Minibatch loss: 12.054, learning rate: 0.010000 Minibatch error: 90.6% Validation error: 84.6% Step 100 (epoch 0.12), 280.2 ms Minibatch loss: 3.287, learning rate: 0.010000 Minibatch error: 6.2% Validation error: 7.0% Step 200 (epoch 0.23), 281.0 ms Minibatch loss: 3.491, learning rate: 0.010000 Minibatch error: 12.5% Validation error: 3.6% Step 300 (epoch 0.35), 281.0 ms Minibatch loss: 3.265, learning rate: 0.010000 Minibatch error: 10.9% Validation error: 3.2% Step 400 (epoch 0.47), 293.0 ms Minibatch loss: 3.221, learning rate: 0.010000 Minibatch error: 7.8% Validation error: 2.7% Step 500 (epoch 0.58), 289.0 ms Minibatch loss: 3.292, learning rate: 0.010000 Minibatch error: 7.8% Validation error: 2.7% Step 600 (epoch 0.70), 287.4 ms Minibatch loss: 3.227, learning rate: 0.010000 Minibatch error: 7.8% Validation error: 2.6% Step 700 (epoch 0.81), 287.0 ms Minibatch loss: 3.015, learning rate: 0.010000 Minibatch error: 3.1% Validation error: 2.4% Step 800 (epoch 0.93), 287.0 ms Minibatch loss: 3.152, learning rate: 0.010000 Minibatch error: 6.2% Validation error: 2.0% Step 900 (epoch 1.05), 287.7 ms Minibatch loss: 2.938, learning rate: 0.009500 Minibatch error: 3.1% Validation error: 1.6% Step 1000 (epoch 1.16), 287.4 ms Minibatch loss: 2.862, learning rate: 0.009500 Minibatch error: 1.6% Validation error: 1.7% . . .
可見程序是跑起來了，但是沒有找到GPU，
reboot #..... source activate tfgpu cd /root/anaconda2/envs/tfgpu/lib/python2.7/site-packages/tensorflow/models/image/mnist/#j進入目錄 python convolutional.py
結果:
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally Extracting data/train-images-idx3-ubyte.gz Extracting data/train-labels-idx1-ubyte.gz Extracting data/t10k-images-idx3-ubyte.gz Extracting data/t10k-labels-idx1-ubyte.gz I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties: name: GeForce 940M major: 5 minor: 0 memoryClockRate (GHz) 1.124 pciBusID 0000:08:00.0 Total memory: 1023.88MiB Free memory: 997.54MiB I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: Y I tensorflow/core/common_runtime/gpu/gpu_device.cc:839] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce 940M, pci bus id: 0000:08:00.0) Initialized! Step 0 (epoch 0.00), 81.3 ms Minibatch loss: 12.054, learning rate: 0.010000 Minibatch error: 90.6% Validation error: 84.6% Step 100 (epoch 0.12), 44.4 ms Minibatch loss: 3.291, learning rate: 0.010000 Minibatch error: 6.2% Validation error: 7.1% Step 200 (epoch 0.23), 44.4 ms Minibatch loss: 3.462, learning rate: 0.010000 Minibatch error: 12.5% Validation error: 3.6% Step 300 (epoch 0.35), 44.0 ms Minibatch loss: 3.188, learning rate: 0.010000 Minibatch error: 4.7% Validation error: 3.2% Step 400 (epoch 0.47), 44.3 ms Minibatch loss: 3.253, learning rate: 0.010000 Minibatch error: 9.4% Validation error: 2.8% Step 500 (epoch 0.58), 44.3 ms Minibatch loss: 3.288, learning rate: 0.010000 Minibatch error: 9.4% Validation error: 2.5% Step 600 (epoch 0.70), 43.9 ms Minibatch loss: 3.180, learning rate: 0.010000 Minibatch error: 6.2% Validation error: 2.8% Step 700 (epoch 0.81), 44.2 ms Minibatch loss: 3.033, learning rate: 0.010000 Minibatch error: 3.1% Validation error: 2.4% Step 800 (epoch 0.93), 44.0 ms Minibatch loss: 3.149, learning rate: 0.010000 Minibatch error: 6.2% Validation error: 2.0% Step 900 (epoch 1.05), 44.0 ms Minibatch loss: 2.919, learning rate: 0.009500 Minibatch error: 3.1% Validation error: 1.6% Step 1000 (epoch 1.16), 43.8 ms Minibatch loss: 2.849, learning rate: 0.009500 Minibatch error: 0.0% Validation error: 1.7% Step 1100 (epoch 1.28), 43.6 ms Minibatch loss: 2.822, learning rate: 0.009500 Minibatch error: 0.0% Validation error: 1.6% Step 1200 (epoch 1.40), 43.6 ms Minibatch loss: 2.979, learning rate: 0.009500 Minibatch error: 7.8% Validation error: 1.5% Step 1300 (epoch 1.51), 43.6 ms Minibatch loss: 2.763, learning rate: 0.009500 Minibatch error: 0.0% Validation error: 1.9% Step 1400 (epoch 1.63), 43.6 ms Minibatch loss: 2.781, learning rate: 0.009500 Minibatch error: 3.1% Validation error: 1.5% Step 1500 (epoch 1.75), 43.6 ms Minibatch loss: 2.861, learning rate: 0.009500 Minibatch error: 6.2% Validation error: 1.4% Step 1600 (epoch 1.86), 43.8 ms Minibatch loss: 2.698, learning rate: 0.009500 Minibatch error: 1.6% Validation error: 1.3% Step 1700 (epoch 1.98), 43.9 ms Minibatch loss: 2.650, learning rate: 0.009500 Minibatch error: 0.0% Validation error: 1.3% Step 1800 (epoch 2.09), 44.1 ms Minibatch loss: 2.652, learning rate: 0.009025 Minibatch error: 1.6% Validation error: 1.3% Step 1900 (epoch 2.21), 44.1 ms Minibatch loss: 2.655, learning rate: 0.009025 Minibatch error: 1.6% Validation error: 1.3% Step 2000 (epoch 2.33), 43.9 ms Minibatch loss: 2.640, learning rate: 0.009025 Minibatch error: 3.1% Validation error: 1.2% Step 2100 (epoch 2.44), 44.0 ms Minibatch loss: 2.568, learning rate: 0.009025 Minibatch error: 0.0% Validation error: 1.1% Step 2200 (epoch 2.56), 44.0 ms Minibatch loss: 2.564, learning rate: 0.009025 Minibatch error: 0.0% Validation error: 1.1% Step 2300 (epoch 2.68), 44.2 ms Minibatch loss: 2.561, learning rate: 0.009025 Minibatch error: 1.6% Validation error: 1.2% Step 2400 (epoch 2.79), 44.2 ms Minibatch loss: 2.500, learning rate: 0.009025 Minibatch error: 0.0% Validation error: 1.3% Step 2500 (epoch 2.91), 44.0 ms Minibatch loss: 2.471, learning rate: 0.009025 Minibatch error: 0.0% Validation error: 1.2% Step 2600 (epoch 3.03), 43.8 ms Minibatch loss: 2.451, learning rate: 0.008574 Minibatch error: 0.0% Validation error: 1.2% Step 2700 (epoch 3.14), 43.6 ms Minibatch loss: 2.483, learning rate: 0.008574 Minibatch error: 1.6% Validation error: 1.1% Step 2800 (epoch 3.26), 43.7 ms Minibatch loss: 2.426, learning rate: 0.008574 Minibatch error: 1.6% Validation error: 1.1% Step 2900 (epoch 3.37), 44.3 ms Minibatch loss: 2.449, learning rate: 0.008574 Minibatch error: 3.1% Validation error: 1.1% Step 3000 (epoch 3.49), 43.9 ms Minibatch loss: 2.395, learning rate: 0.008574 Minibatch error: 1.6% Validation error: 1.0% Step 3100 (epoch 3.61), 44.1 ms Minibatch loss: 2.390, learning rate: 0.008574 Minibatch error: 3.1% Validation error: 1.0% Step 3200 (epoch 3.72), 43.6 ms Minibatch loss: 2.330, learning rate: 0.008574 Minibatch error: 0.0% Validation error: 1.1% Step 3300 (epoch 3.84), 43.8 ms Minibatch loss: 2.319, learning rate: 0.008574 Minibatch error: 1.6% Validation error: 1.1% Step 3400 (epoch 3.96), 44.4 ms Minibatch loss: 2.296, learning rate: 0.008574 Minibatch error: 0.0% Validation error: 1.0% Step 3500 (epoch 4.07), 44.4 ms Minibatch loss: 2.273, learning rate: 0.008145 Minibatch error: 0.0% Validation error: 1.0% Step 3600 (epoch 4.19), 44.2 ms Minibatch loss: 2.253, learning rate: 0.008145 Minibatch error: 0.0% Validation error: 0.9% Step 3700 (epoch 4.31), 44.4 ms Minibatch loss: 2.237, learning rate: 0.008145 Minibatch error: 0.0% Validation error: 1.0% Step 3800 (epoch 4.42), 43.8 ms Minibatch loss: 2.234, learning rate: 0.008145 Minibatch error: 1.6% Validation error: 0.9% Step 3900 (epoch 4.54), 43.9 ms Minibatch loss: 2.325, learning rate: 0.008145 Minibatch error: 3.1% Validation error: 0.9% Step 4000 (epoch 4.65), 43.6 ms Minibatch loss: 2.215, learning rate: 0.008145 Minibatch error: 0.0% Validation error: 1.1% Step 4100 (epoch 4.77), 43.6 ms Minibatch loss: 2.209, learning rate: 0.008145 Minibatch error: 1.6% Validation error: 1.0% Step 4200 (epoch 4.89), 43.6 ms Minibatch loss: 2.242, learning rate: 0.008145 Minibatch error: 1.6% Validation error: 1.0% Step 4300 (epoch 5.00), 43.5 ms Minibatch loss: 2.188, learning rate: 0.007738 Minibatch error: 1.6% Validation error: 0.9% Step 4400 (epoch 5.12), 43.5 ms Minibatch loss: 2.155, learning rate: 0.007738 Minibatch error: 3.1% Validation error: 1.0% Step 4500 (epoch 5.24), 43.5 ms Minibatch loss: 2.164, learning rate: 0.007738 Minibatch error: 4.7% Validation error: 0.9% Step 4600 (epoch 5.35), 43.5 ms Minibatch loss: 2.095, learning rate: 0.007738 Minibatch error: 0.0% Validation error: 0.9% Step 4700 (epoch 5.47), 43.6 ms Minibatch loss: 2.062, learning rate: 0.007738 Minibatch error: 0.0% Validation error: 0.9% Step 4800 (epoch 5.59), 43.6 ms Minibatch loss: 2.068, learning rate: 0.007738 Minibatch error: 1.6% Validation error: 1.0% Step 4900 (epoch 5.70), 43.6 ms Minibatch loss: 2.062, learning rate: 0.007738 Minibatch error: 1.6% Validation error: 1.0% Step 5000 (epoch 5.82), 43.5 ms Minibatch loss: 2.148, learning rate: 0.007738 Minibatch error: 3.1% Validation error: 1.0% Step 5100 (epoch 5.93), 43.5 ms Minibatch loss: 2.017, learning rate: 0.007738 Minibatch error: 1.6% Validation error: 0.9% Step 5200 (epoch 6.05), 43.5 ms Minibatch loss: 2.074, learning rate: 0.007351 Minibatch error: 3.1% Validation error: 1.0% Step 5300 (epoch 6.17), 43.6 ms Minibatch loss: 1.983, learning rate: 0.007351 Minibatch error: 0.0% Validation error: 1.1% Step 5400 (epoch 6.28), 43.6 ms Minibatch loss: 1.957, learning rate: 0.007351 Minibatch error: 0.0% Validation error: 0.8% Step 5500 (epoch 6.40), 43.5 ms Minibatch loss: 1.955, learning rate: 0.007351 Minibatch error: 0.0% Validation error: 0.9% Step 5600 (epoch 6.52), 43.5 ms Minibatch loss: 1.926, learning rate: 0.007351 Minibatch error: 0.0% Validation error: 0.8% Step 5700 (epoch 6.63), 43.5 ms Minibatch loss: 1.914, learning rate: 0.007351 Minibatch error: 0.0% Validation error: 1.0% Step 5800 (epoch 6.75), 43.6 ms Minibatch loss: 1.897, learning rate: 0.007351 Minibatch error: 0.0% Validation error: 0.9% Step 5900 (epoch 6.87), 43.5 ms Minibatch loss: 1.887, learning rate: 0.007351 Minibatch error: 0.0% Validation error: 0.8% Step 6000 (epoch 6.98), 43.6 ms Minibatch loss: 1.878, learning rate: 0.007351 Minibatch error: 0.0% Validation error: 1.0% Step 6100 (epoch 7.10), 43.5 ms Minibatch loss: 1.859, learning rate: 0.006983 Minibatch error: 0.0% Validation error: 0.8% Step 6200 (epoch 7.21), 43.6 ms Minibatch loss: 1.844, learning rate: 0.006983 Minibatch error: 0.0% Validation error: 0.8% Step 6300 (epoch 7.33), 43.6 ms Minibatch loss: 1.850, learning rate: 0.006983 Minibatch error: 1.6% Validation error: 0.9% Step 6400 (epoch 7.45), 43.6 ms Minibatch loss: 1.916, learning rate: 0.006983 Minibatch error: 3.1% Validation error: 0.8% Step 6500 (epoch 7.56), 43.6 ms Minibatch loss: 1.808, learning rate: 0.006983 Minibatch error: 0.0% Validation error: 0.8% Step 6600 (epoch 7.68), 43.5 ms Minibatch loss: 1.839, learning rate: 0.006983 Minibatch error: 1.6% Validation error: 0.9% Step 6700 (epoch 7.80), 43.6 ms Minibatch loss: 1.781, learning rate: 0.006983 Minibatch error: 0.0% Validation error: 0.8% Step 6800 (epoch 7.91), 43.6 ms Minibatch loss: 1.773, learning rate: 0.006983 Minibatch error: 0.0% Validation error: 0.8% Step 6900 (epoch 8.03), 43.5 ms Minibatch loss: 1.762, learning rate: 0.006634 Minibatch error: 0.0% Validation error: 0.9% Step 7000 (epoch 8.15), 43.5 ms Minibatch loss: 1.797, learning rate: 0.006634 Minibatch error: 1.6% Validation error: 0.9% Step 7100 (epoch 8.26), 43.5 ms Minibatch loss: 1.741, learning rate: 0.006634 Minibatch error: 0.0% Validation error: 0.8% Step 7200 (epoch 8.38), 43.5 ms Minibatch loss: 1.744, learning rate: 0.006634 Minibatch error: 0.0% Validation error: 0.9% Step 7300 (epoch 8.49), 43.6 ms Minibatch loss: 1.726, learning rate: 0.006634 Minibatch error: 1.6% Validation error: 0.8% Step 7400 (epoch 8.61), 43.5 ms Minibatch loss: 1.704, learning rate: 0.006634 Minibatch error: 0.0% Validation error: 0.8% Step 7500 (epoch 8.73), 43.6 ms Minibatch loss: 1.695, learning rate: 0.006634 Minibatch error: 0.0% Validation error: 0.8% Step 7600 (epoch 8.84), 43.5 ms Minibatch loss: 1.808, learning rate: 0.006634 Minibatch error: 3.1% Validation error: 0.8% Step 7700 (epoch 8.96), 43.6 ms Minibatch loss: 1.667, learning rate: 0.006634 Minibatch error: 0.0% Validation error: 0.9% Step 7800 (epoch 9.08), 43.5 ms Minibatch loss: 1.660, learning rate: 0.006302 Minibatch error: 0.0% Validation error: 0.9% Step 7900 (epoch 9.19), 43.6 ms Minibatch loss: 1.649, learning rate: 0.006302 Minibatch error: 0.0% Validation error: 0.9% Step 8000 (epoch 9.31), 43.5 ms Minibatch loss: 1.666, learning rate: 0.006302 Minibatch error: 0.0% Validation error: 0.8% Step 8100 (epoch 9.43), 43.6 ms Minibatch loss: 1.626, learning rate: 0.006302 Minibatch error: 0.0% Validation error: 0.8% Step 8200 (epoch 9.54), 43.5 ms Minibatch loss: 1.633, learning rate: 0.006302 Minibatch error: 1.6% Validation error: 0.8% Step 8300 (epoch 9.66), 43.6 ms Minibatch loss: 1.616, learning rate: 0.006302 Minibatch error: 0.0% Validation error: 0.8% Step 8400 (epoch 9.77), 43.6 ms Minibatch loss: 1.597, learning rate: 0.006302 Minibatch error: 0.0% Validation error: 0.8% Step 8500 (epoch 9.89), 43.5 ms Minibatch loss: 1.612, learning rate: 0.006302 Minibatch error: 1.6% Validation error: 0.8% Test error: 0.8%
Finally Dode!!!
總結來來回回折騰了4天。教訓就是一定要根據官網一步一步來，因為不同版本兼容性不行，所以不要隨意下載其他版本,同時要仔細分析報出的錯誤，再采取下一步行動。
歡迎訪問我的主頁（http://mageek.cn/）
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