Win7 64位 Anaconda Lasagne 安装教程，防止错误，另Lasagne人脸facial points detect

一、Win7 64 安装theano：

1、 下载Anaconda1.9.2，自带MniGw；

C:\Anaconda;

C:\Anaconda\Scripts;C

:\Anaconda\MinGW\bin;

C:\Anaconda\MinGW\x86_64-w64-mingw32\bin;加入到PATH



安装git,并将C:\git\bin;加入到PATH

2、 Github下载theano然后解压，讲theano文件夹里面的theano文件夹拷贝到

C:\Anaconda\Lib\site-packages

3、 下载安装CUDA，版本随意

将C:\Program Files (x86)\NVIDIA Corporation\PhysX\Common;

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0\bin;

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0\libnvvp;

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0\lib\x64;

C:\Program Files (x86)\Microsoft SDKs\Windows\v7.0A\Include;

加入到path。

测试Cuda安装成功与否，使用nvcc –V看cuda版本



采用VS2010，将C:\Program Files (x86)\Microsoft SDKs\Windows\v7.0A\Include;加入到PATH，则最终PATH有：

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v6.0\bin;

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v6.0\libnvvp;

c:\Program Files (x86)\NVIDIA Corporation\PhysX\Common;

C:\Anaconda;

C:\Anaconda\Scripts;

C:\Anaconda\MinGW\bin;

C:\Anaconda\MinGW\x86_64-w64-mingw32\bin;

C:\git\bin;

C:\Program Files (x86)\Microsoft SDKs\Windows\v7.0A\Include;



4、 在C:\Users\Administrator下建立一个文件：.theanorc.txt

[global]

openmp = False

device = gpu0

floatX = float32

allow_input_downcast=True



[blas]

ldflags =



[gcc]

cxxflags = -IC:\Anaconda\MinGW





[cuda]

root=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0\bin



[nvcc]

flags = -LC:\Anaconda\libs



compiler_bindir = C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\bin

fastmath = True

flags = -arch=sm_30



注意错误：

1、c:\anaconda\include\pyconfig.h(227) : fatal error C1083: Cannot open include fil

e: 'basetsd.h': No such file or directory；

ERROR (theano.sandbox.cuda): Failed to compile cuda_ndarray.cu: ('nvcc return st

atus', 2, 'for cmd', 'nvcc -shared -O3 -arch=sm_30 -use_fast_math --compiler-bin



根据windows SDK查找这个文件，将这个文件对应的include加入到PATH，没有解决问题，后来compiler_bindir Vs2012更改为Vs2010解决问题。compiler_bindir = C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\bin



二、安装Lasagne

1、目前还只能从源代码安装。

git clone https://github.com/Lasagne/Lasagne.git # 会建立一个Lasagne目录

cd Lasagne

pip install -r requirements.txt # 比较久

python setup.py install # 这一步需要root权限



Daniel Nouri[Tutorial]上是这样安装的

pip install -r https://raw.githubusercontent.com/dnouri/kfkd-tutorial/master/requirements.txt
这样需要以root用户来执行，不推荐这样做。



尝试正确的安装方式：

git clone https://github.com/Lasagne/Lasagne.git # 会建立一个Lasagne目录

cd Lasagne

pip install -r https://raw.githubusercontent.com/dnouri/kfkd-tutorial/master/requirements.txt


问题：

1. 这是Python 2 mimetypes的bug

2. 需要将Python2.7/lib/mimetypes.py文件中如下片段注释或删除：

try:

ctype = ctype.encode(default_encoding) # omit in 3.x!

except UnicodeEncodeError:

pass

补充其它解决办法

解决办法：

在报错的页面添加代码： import sys

reload(sys)

sys.setdefaultencoding('utf8')

执行 Python ez_setup.py，报错：

UnicodeDecodeError: 'utf8' codec can't decode byte 0xb0 in position 35: invalid

start byte

解决办法：

在报错的页面添加代码：

import sys

reload(sys)

sys.setdefaultencoding('utf-8')



安装失败之后，重新安装，需要清理C:\Users\Administrator\AppData\Local\pip里面的内容



2、test on mnist

cd examples

python mnist.py

Epoch 103 of 500 took 11.717s

training loss: 0.045202

validation loss: 0.059163

validation accuracy: 98.16 %%

Epoch 104 of 500 took 11.702s

training loss: 0.046228

validation loss: 0.058582

validation accuracy: 98.14 %%

Epoch 105 of 500 took 11.704s

training loss: 0.044530

validation loss: 0.058295

validation accuracy: 98.18 %%



三、Facial Keypoints Detection

1、从https://github.com/dylansun/Kaggle-Facial-Keypoint-Detection 上下载data数据进行训练：

Data Files：training .zip (60.10 mb)test .zip (15.99 mb)SampleSubmission .csv (201.08 kb)IdLookupTable .csv (842.51 kb)

Each predicted keypoint is specified by an (x,y) real-valued pair in the space of pixel indices. There are 15 keypoints, which represent the following elements of the face:

left_eye_center, right_eye_center, left_eye_inner_corner, left_eye_outer_corner, right_eye_inner_corner, right_eye_outer_corner, left_eyebrow_inner_end, left_eyebrow_outer_end, right_eyebrow_inner_end, right_eyebrow_outer_end, nose_tip, mouth_left_corner, mouth_right_corner, mouth_center_top_lip, mouth_center_bottom_lip



Left and right here refers to the point of view of the subject.



In some examples, some of the target keypoint positions are misssing (encoded as missing entries in the csv, i.e., with nothing between two commas).



The input image is given in the last field of the data files, and consists of a list of pixels (ordered by row), as integers in (0,255). The images are 96x96 pixels.



Data files

training.csv: list of training 7049 images. Each row contains the (x,y) coordinates for 15 keypoints, and image data as row-ordered list of pixels.

test.csv: list of 1783 test images. Each row contains ImageId and image data as row-ordered list of pixels

submissionFileFormat.csv: list of 27124 keypoints to predict. Each row contains a RowId, ImageId, FeatureName, Location. FeatureName are "left_eye_center_x," "right_eyebrow_outer_end_y," etc. Location is what you need to predict.



2、采用kfkd.py进行训练：

"""

To use this script, first run this to fit your first model:

python kfkd.py fit

Then train a bunch of specialists that intiliaze their weights from

your first model:

python kfkd.py fit_specialists net.pickle

Plot their error curves:

python kfkd.py plot_learning_curves net-specialists.pickle

此处报错：

Traceback (most recent call last):

File "C:\Program Files (x86)\JetBrains\PyCharm 4.0.4\helpers\pydev\pydevd.py", line 2222, in <module>

globals = debugger.run(setup['file'], None, None)

File "C:\Program Files (x86)\JetBrains\PyCharm 4.0.4\helpers\pydev\pydevd.py", line 1648, in run

pydev_imports.execfile(file, globals, locals) # execute the script

File "C:/Anaconda/src/lasagne/examples/Kaggle Facial Keypoints Detection/kfkd.py", line 417, in <module>

func(*sys.argv[2:])

File "C:/Anaconda/src/lasagne/examples/Kaggle Facial Keypoints Detection/kfkd.py", line 371, in plot_learning_curves

models = pickle.load(f)

EOFError

解决方法：直接执行plot_learning_curves




And finally make predictions to submit to Kaggle:

python kfkd.py predict net-specialists.pickle