Add (failing) tests for MNIST target iteration
Fix MNIST target bug and deprecate one_hot parameter
Remove useless comment
Format documentation of a few models

autoencoder, differentiable sparse coding, gsn, local coordinate coding
and maxout
Format documentation (mlp, ... pca)

mlp, model, normalized_ebm, pca
Format documentation in RBM

Should all parameters with a default value (including None) be
considered as optional?

The parameters in the docstring of class mu_pooled_ssRBM differ from
those in __init__(). Should the docstring be modified?
Format doc in S3C

In class S3C, will local_rf_draw_patches always initialize the weights
to have only local receptive fields, or will it do so only when True?
Format doc for other models

DBN not done yet
Format doc for DBMs (and small changes in MLP)
Format doc in batch_gradient descent

One docstring was replaced by "#" comments to avoid displaying the fake
function "beta = None" in the online documentation.

There is also a minor change in models/RBM.
Format doc in optimization (partially)

In linear_cg, I put the reference to Wikipedia in the summary section.
"See also" expects another function from the library. When you put
something else there, you get either weird documentation or an error.
The "Notes" section doesn't seem to accept hyperlinks. Finally, in the
Numpy docstring standard, it is discouraged to put web pages in the
"References" section.
Format doc in minres

The enumerated list in the "Returns" field was technically ok, but it
looked weird.
Format documentation in scripts

For most files, I had to put the module doctring right at the beginning
(or just after the shebang when there was one).

There are also minor modifications to /optimization/linesearch.py
Format documentation in Space

There are some functions where there is both a docstring and a "@wraps"
decorator. I don't know which one is right. I should file a ticket.
in space._cast(), added cases for when batch is numeric CudaNdarray or symbolic CudaNdarrayType
fixed space._cast() to properly handle numeric and symbolic CudaNdarray batches (i.e. no-op if trying to cast to floatX, TypeError otherwise)
CudaNdarrays only support float32, not theano.config.floatX. Fixed space._cast() accordingly.
fixed VectorSpace._validate_impl(). When calling _valdiate_impl(S) on a symbolic batch S, it was calling validate() on get_debug_values(S), which returns numeric batches. Fixed by replacing that validate() with np_validate().
Edited the stacked autoencoder tutorial
Fix some of the pep8 errors

Excludes PEP8 errors in models/tests/test_conv_relu.py