implementation of two linesearches in theano

one of them is based on only evaluating the function, the other
one respects the strong wolfe condition and implies also computing
the gradient with respect to the scalar we optimize.
pointer to the code I inspired myself from
Rop for FilterActs
Rop for pooling
missing cuda code for rop and WIP test file
add warning for the test
visualization script for norb dataset
convenience link to Dropout class
input validation
fixed width of binocular weights viewer
unit test of image activations gradient
removed commented code
get_choice
Merge branch 'master' of github.com:lisa-lab/pylearn2
Fixes some ReST doc syntax.
added min_class parameter to DenseDesignMatrix.convert_to_one_hot
yaml convenience functions
added restrict_instances parameter to FoveatedNORB
doc and training fold restriction for STL-10
checks of validity of InferenceProcedure's output