It seems that even the last fix of the generated random numbers didn't do
the trick. The reason being same numeric instability.
I've change the operation to a sum, which, if you apply it recursevly is
less problematic then multiplying numbers

This test fails because of small numbers (the gradients are especially
susceptible to small numbers). Increasing the range from which we sample,
it doesn't mean that there will not be a seed there for which the sampled
numbers are small enough (or a few entries are small enough) to get in that
instability region

This tests are of the sandbox of scan that I want to change anyway..

Improved scan error message

I think that we can't really use iter(x) as suggested by David, because we want to iterate over the items.

Two improvements:
- An explicit error is raised if the lambda expression used in scan
  returns something that is not made of Theano variables (which may
  happen for instance when someone returns a constant value).
- An error is always raised when failing to parse the return value of
  the lambda expression

Fixed gh-356: dtype of tensor.sum()

Fixed some references to code recently moved

This is to accomodate changes from f9ca8f9d.

Gradient code is moved from tensor/tensor_grad.py to theano/gradient.py.
This makes it work with sparse variables.

This commit was originally written by Arnaud Bergeron. I re-authored it
to avoid a big merge in repo history.

Code for sparse was stolen from Yann Dauphin.

Make mlp_test test a more sensible case.

Previous test was testing settings where we removed either the
"local_track_shape_i" or "local_shape_to_shape_i" optimizations.
These are unlikely settings, what we test now is the complete
removal of the "ShapeOpt" optimization.

Fixed grad of grad of scan

Fixed tests with script run_tests_in_batch.py

Note sure it makes the file anymore readable, but at least I've tried.

Otherwise the numberic differentiation gradient quickly becomes numerically
unstable.

the index goes from 0 to n_mit_mot-1 for mit_mot sequnces.

The main bug was gradients where represented as shared variables.
Now we represent them as sit_sot sequences to which only the last step
is used (hence the savemem optimization does the memory clean up). The
advantage is that gradients with respect to sitsot are well defined.

When collecting shapes from the outputs of the inner function, we should
keep track that mit_mot arguments have multiple outputs for one argument.

The error was raised if either the dtype didn't match or ndim didn't match.
However the error message did not display the ndims.

Added missing __init__.py so that tests can be imported by nose.

All tests are now passing under Windows with Python 2.4.

Fixed max/argmax gradient tests in float32

fix how we compute the number of elements in the compile dir.

Work around bug in numpy 1.6 with ufunc.reduce