UFLDL sparse auto-encoder exercise.

The hardest part is to write the backpropagation, with that done, everything is fine.

Not sure why the final training always hit maximum-iteration 400 .
but the final visualization is fine.

python google class.

python strings:

-------------------------------------------------------------------------

1. 

built in string class named str, single quote or double quote both OK. use """ for multiple line string. 

2. 

string are immutable. 

3. 

zero-based indexing for accessing string with [] 

ex: 

str = 'hello'   str[1] is e

4. 

don't use "len" as variable name!

5. 

+ to cat two strings. 

6.

a raw string : 

raw = r'this\t\n and that'

print raw

7. 

string slicing:

s = 'hello' 

print s[1:4]

print s[1:]

print s[:]

print s[1:100]

note the last one. 

8. 

string methods modifies the original string. 

print list.append(4) # this doesn't work 

python sorting 

---------------------------------------------------------------------------------

1. 

sorted(a)  doesn't change the original list.

2. 

sorted() method is recommended over the old sort() method, because the former can take as input any iterable collection .

3. 

sorted() string compare their ascii number lexicographically. 

capital letters are smaller

4. 

sort by length:

strs = ['ccc', 'aaa', 'd', 'bb']

print sorted(strs, key=len)

5. 

custom key function. 

strs = ['xc', 'zb', 'yd', 'wa']

def myFn(s):

    return s[-1]

print sorted(strs, key = myFn)

Tuples

-----------------------------------------------------------------

1.

fixed size grouping of elements. 

2. 

immutable and don't change size

3. 

function that returns fixed number multiple values may just return a tuple.

4.

tuple = (1,2,'hi')

print len(tuple)

print tuple[2]

tuple[2] = 'bye' # not going to work!!!!!!

tuple = (1,2, 'bye') # this works

5.

create a single element tuple, the lone element has to be followed by a comma:

tuple = ('hi',)

to distinguish a tuple from a parenthesized string. 

----------------------------------

done 

Installation of Theano.

I am learning Deep learning, the first thing to do is to install Theano. This is the process:
1.
 go to the link: http://deeplearning.net/software/theano/install.html#install 

2.
 I get this error: dfftpack missing fortran complier. I download the gfortran pkg for mac. install gfortran

 3. run sudo pip install Theano
now it is running for gfortran...
but still has a lot of errors.

4. then I get a lot of erros saying missing dependencies of scipy

5. run the command sudo port install py27-numpy +atlas py27-scipy +atlas py27-pip 

but I get these two errors:

Error: org.macports.build for port llvm-3.3 returned: command execution failed
Error: Failed to install llvm-3.3

6.
run sudo port install llvm-2.9 



==================================
alright, so I failed on installing it on mac, spending the whole afternoon on ubuntu 12.04.

1. install libblas, liblapack from the software center.
2. install scipy using command  python setup.py install --user this install the scipy to my local user .
3. install numpy similarly.
4. install python nose from software center.
5. then I can test numpy using python -c "import numpy;  numpy.test()"  to get the following output:

NumPy version 1.7.0
NumPy is installed in /fs/narahomes/wenhoujx/.local/lib/python2.7/site-packages/numpy
Python version 2.7.3 (default, Aug  1 2012, 05:14:39) [GCC 4.6.3]
nose version 1.1.2

6. test scipy using python -c "import scipy; scipy.test()"    the output: 

Running unit tests for scipy

NumPy version 1.7.0

NumPy is installed in /fs/narahomes/wenhoujx/.local/lib/python2.7/site-packages/numpy

SciPy version 0.12.0

SciPy is installed in /fs/narahomes/wenhoujx/.local/lib/python2.7/site-packages/scipy

Python version 2.7.3 (default, Aug  1 2012, 05:14:39) [GCC 4.6.3]

nose version 1.1.2

/fs/narahomes/wenhoujx/.local/lib/python2.7/site-packages/numpy/lib/utils.py:139: DeprecationWarning: `scipy.lib.blas` is deprecated, use `scipy.linalg.blas` instead!

  warnings.warn(depdoc, DeprecationWarning)

/fs/narahomes/wenhoujx/.local/lib/python2.7/site-packages/numpy/lib/utils.py:139: DeprecationWarning: `scipy.lib.lapack` is deprecated, use `scipy.linalg.lapack` instead!

  warnings.warn(depdoc, DeprecationWarning)

7. now install theano, download from https://pypi.python.org/pypi/Theano#downloads

then tar, python setup.py install --user

8. test theano 

wenhoujx@narawks16:/scratch0/pylib$ python -c "import theano; theano.test()"
Theano version 0.6.0rc3
theano is installed in /fs/narahomes/wenhoujx/.local/lib/python2.7/site-packages/Theano-0.6.0rc3-py2.7.egg/theano
NumPy version 1.7.0
NumPy is installed in /fs/narahomes/wenhoujx/.local/lib/python2.7/site-packages/numpy
Python version 2.7.3 (default, Aug  1 2012, 05:14:39) [GCC 4.6.3]
nose version 1.1.2

----------------------------------------------------------------------
Ran 0 tests in 0.002s

OK

there is a problem that the test doesn't run . zero test.

9. pip uninstall theano    to clear theano
then pip install --user theano to install it in $HOME.
then run python -c "import theano; theano.test()"
now everything is running, but with warnings.

it will probably take ~30min, it is 9:24pm now.
finish on 10:31 pm

Ran 2015 tests in 3131.603s

10. done.

tar error

download a foo.tar.gz

unzip foo.tar.gz

tar -xzf foo.tar  get the error:

gzip: stdin: not in gzip format
tar: Child returned status 1
tar: Error is not recoverable: exiting now

solution:
I changed the foo.tar.gz to foo.tar then tar -zxf foo.tar to get the untarred folder.

source: https://bbs.archlinux.org/viewtopic.php?id=98278
Maybe it's just a simple tar archive and not gzipped and tar does detect this but file roller does not. Have you tried renaming it to archname.tar and then installing it -- maybe gzipping it in between?

install theano