.. _tutsparse:

======
Sparse
======

This is a very short tutorial on sparse matrices with Theano. There is still
some not well documented behavior like how we take care of the
gradient. There are 2 types of gradient for sparse operations. ``full
gradient`` that compute a gradient for values even if they were 0 and
the ``structured gradient`` that returns a gradient only for values
that were not 0. You need to check the code to know which gradient an
Op implements.

More documentation in the :ref:`Sparse Library Reference <libdoc_sparse>`.

A small example:

.. code-block:: python

    import theano
    import theano.tensor as T
    import scipy.sparse as sp
    import theano.sparse as S
    import numpy as np

    x = S.csr_matrix ('x')
    #x = T.matrix ('x')
    y = T.matrix ('y')
    z = S.dot (x, y)
    f = theano.function ([x, y], z)

    #a = np.array ([[0, 1], [1, 0], [1, 0], [0, 1]], dtype=np.float32)
    a = sp.coo_matrix (([1] * 4, (range (4), [0, 1, 1, 0])), dtype=np.float32)

    b = np.array ([[10, 11], [12, 13]], dtype=np.float32)

    print f (a, b)
