
.. _lib_scan:

================================
:mod:`scan` -- Looping in Theano
================================


Guide
=====

The scan functions provides the basic functionality needed to do loops
in Theano. Scan comes with many whistles and bells, which we will introduce
by way of examples.


Simple loop with accumulation:  Computing :math:`A^k`
-----------------------------------------------------

Assume that, given *k* you want to get ``A**k`` using a loop.
More precisely, if *A* is a tensor you want to compute
``A**k`` elemwise. The python/numpy code might look like:

.. code-block:: python

  result = 1
  for i in xrange(k):
    result = result * A

There are three thing here that we need to handle: the initial value
assigned to ``result``, the accumulation of results in ``result``, and
the unchanging variable ``A``. Unchanging variables are passed to scan as
``non_sequences``. Initialization occurs in ``outputs_info``, and the accumulation
happens automatically.

The equivalent Theano code would be:

.. code-block:: python

  # Symbolic description of the result
  result, updates = theano.scan(fn=lambda prior_result, A: prior_result * A,
                                outputs_info=T.ones_like(A),
                                non_sequences=A,
                                n_steps=k)

  # We only care about A**k, but scan has provided us with A**1 through A**k.
  # Discard the values that we don't care about. Scan is smart enough to
  # notice this and not waste memory saving them.
  final_result = result[-1]

  # compiled function that returns A**k
  power = theano.function(inputs=[A,k], outputs=final_result, updates=updates)

Let us go through the example line by line. What we did is first to
construct a function (using a lambda expression) that given ``prior_result`` and
``A`` returns ``prior_result * A``. The order of parameters is fixed by scan:
the output of the prior call to ``fn`` (or the initial value, initially)
is the first parameter, followed by all non-sequences.

Next we initialize the output as a tensor with same shape and dtype as ``A``,
filled with ones. We give ``A`` to scan as a non sequence parameter and
specify the number of steps ``k`` to iterate over our lambda expression.

Scan return a tuples, containing our result (``result``) and a
dictionary of updates (empty in this case). Note that the result
is not a matrix, but a 3D tensor containing the value of ``A**k`` for
each step. We want the last value (after ``k`` steps) so we compile
a function to return just that. Note that there is an optimization, that
at compile time will detect that you are using just the last value of the
result and ensure that scan does not store all the intermediate values
that are used. So do not worry if ``A`` and ``k`` are large.


Iterating over the first dimension of a tensor: Calculating a polynomial
------------------------------------------------------------------------
In addition to looping a fixed number of times, scan can iterate over
the leading dimension of tensors (similar to Python's ``for x in a_list``).

The tensor(s) to be looped over should be provided to scan using the
``sequence`` keyword argument.

Here's an example that builds a symbolic calculation of a polynomial
from a list of its coefficients:

.. code-block:: python

    coefficients = theano.tensor.vector("coefficients")
    x = T.scalar("x")

    max_coefficients_supported = 10000

    # Generate the components of the polynomial
    components, updates = theano.scan(fn=lambda coefficient, power, free_variable: coefficient * (free_variable ** power),
                                      outputs_info=None,
                                      sequences=[coefficients, theano.tensor.arange(max_coefficients_supported)],
                                      non_sequences=x)
    # Sum them up
    polynomial = components.sum()

    # Compile a function
    calculate_polynomial = theano.function(inputs=[coefficients, x], outputs=polynomial)

    # Test
    test_coefficients = numpy.asarray([1, 0, 2], dtype=numpy.float32)
    test_value = 3
    print calculate_polynomial(test_coefficients, test_value)
    print 1.0 * (3 ** 0) + 0.0 * (3 ** 1) + 2.0 * (3 ** 2) 

There are a few things to note here.

First, we calculate the polynomial by first generating each of the coefficients, and
then summing them at the end. (We could also have accumulated them along the way, and then
taken the last one, which would have been more memory-efficient, but this is an example.)

Second, there is no accumulation of results, we can set ``outputs_info`` to ``None``. This indicates
to scan that it doesn't need to pass the prior result to ``fn``.

The general order of function parameters to ``fn`` is::

    sequences (if any), prior result(s) (if needed), non-sequences (if any)

Third, there's a handy trick used to simulate python's ``enumerate``: simply include
``theano.tensor.arange`` to the sequences.

Fourth, given multiple sequences of uneven lengths, scan will truncate to the shortest of them.
This makes it safe to pass a very long arange, which we need to do for generality, since
arange must have its length specified at creation time.


Simple accumulation into a scalar, ditching lamba
-------------------------------------------------

This should be fairly self-explanatory.

.. code-block:: python

    up_to = T.iscalar("up_to")

    # define a named function, rather than using lambda
    def accumulate_by_adding(arange_val, sum_to_date):
        return sum_to_date + arange_val

    scan_result, scan_updates = theano.scan(fn=accumulate_by_adding,
                                            outputs_info=T.as_tensor_variable(0),
                                            sequences=T.arange(up_to))
    triangular_sequence = theano.function(inputs=[up_to], outputs=scan_result)

    # test
    some_num = 15
    print triangular_sequence(some_num)
    print [n * (n + 1) // 2 for n in xrange(some_num)]



Another simple example
----------------------

Unlike some of the prior examples, this one is hard to reproduce except by using scan.

This takes a sequence of array indices, and values to place there,
and a "model" output array (whose shape and dtype will be mimicked),
and produces a sequence of arrays with the shape and dtype of the model,
with all values set to zero except at the provided array indices.

.. code-block:: python

    location = T.imatrix("location")
    values = T.vector("values")
    output_model = T.matrix("output_model")

    def set_value_at_position(a_location, a_value, output_model):
        zeros = T.zeros_like(output_model)
        zeros_subtensor = zeros[a_location[0], a_location[1]]
        return T.set_subtensor(zeros_subtensor, a_value)

    result, updates = theano.scan(fn=set_value_at_position,
                                  outputs_info=None,
                                  sequences=[location, values],
                                  non_sequences=output_model)

    assign_values_at_positions = theano.function(inputs=[location, values, output_model], outputs=result)

    # test
    test_locations = numpy.asarray([[1, 1], [2, 3]], dtype=numpy.int32)
    test_values = numpy.asarray([42, 50], dtype=numpy.float32)
    test_output_model = numpy.zeros((5, 5), dtype=numpy.float32)
    print assign_values_at_positions(test_locations, test_values, test_output_model)

This demonstrates that you can introduce new Theano variables into a scan function.


Multiple outputs, several taps values - Recurrent Neural Network with Scan
--------------------------------------------------------------------------

The examples above showed simple uses of scan. However, scan also supports
referring not only to the prior result and the current sequence value, but
also looking back more than one step.

This is needed, for example, to implement a RNN using scan. Assume
that our RNN is defined as follows :

.. math::
  x(n) = \tanh( W x(n-1) + W^{in}_1 u(n) + W^{in}_2 u(n-4) +
  W^{feedback} y(n-1) )

  y(n) = W^{out} x(n- 3)

Note that this network is far from a classical recurrent neural
network and might be useless. The reason we defined as such
is to better illustrate the features of scan.

In this case we have a sequence over which we need to iterate ``u``,
and two outputs ``x`` and ``y``. To implement this with scan we first
construct a function that computes one iteration step :

.. code-block:: python

  def oneStep(u_tm4, u_t, x_tm3, x_tm1, y_tm1, W, W_in_1, W_in_2,  W_feedback, W_out):

    x_t = T.tanh( theano.dot(x_tm1, W) + \
                  theano.dot(u_t,   W_in_1) + \
                  theano.dot(u_tm4, W_in_2) + \
                  theano.dot(y_tm1, W_feedback))
    y_t = theano.dot(x_tm3, W_out)

    return [x_t, y_t]

As naming convention for the variables we used ``a_tmb`` to mean ``a`` at
``t-b`` and ``a_tpb`` to be ``a`` at ``t+b``.
Note the order in which the parameters are given, and in which the
result is returned. Try to respect chronological order among
the taps ( time slices of sequences or outputs) used. For scan is crucial only
for the variables representing the different time taps to be in the same order
as the one in which these taps are given. Also, not only taps should respect
an order, but also variables, since this is how scan figures out what should
be represented by what. Given that we have all
the Theano variables needed we construct our RNN as follows :

.. code-block:: python

   u  = T.matrix() # it is a sequence of vectors
   x0 = T.matrix() # initial state of x has to be a matrix, since
                   # it has to cover x[-3]
   y0 = T.vector() # y0 is just a vector since scan has only to provide
                   # y[-1]


   ([x_vals, y_vals],updates) = theano.scan(fn = oneStep, \
                                sequences    = dict(input = u, taps= [-4,-0]), \
                                outputs_info = [dict(initial = x0, taps = [-3,-1]),y0], \
                                non_sequences  = [W,W_in_1,W_in_2,W_feedback, W_out])
        # for second input y, scan adds -1 in output_taps by default



Now ``x_vals`` and ``y_vals`` are symbolic variables pointing to the
sequence of x and y values generated by iterating over u. The
``sequence_taps``, ``outputs_taps`` give to scan information about what
slices are exactly needed. Note that if we want to use ``x[t-k]`` we do
not need to also have ``x[t-(k-1)], x[t-(k-2)],..``, but when applying
the compiled function, the numpy array given to represent this sequence
should be large enough to cover this values. Assume that we compile the
above function, and we give as ``u`` the array ``uvals = [0,1,2,3,4,5,6,7,8]``.
By abusing notations, scan will consider ``uvals[0]`` as ``u[-4]``, and
will start scaning from ``uvals[4]`` towards the end.


Using shared variables - Gibbs sampling
---------------------------------------

Another useful feature of scan, is that it can handle shared variables.
For example, if we want to implement a Gibbs chain of length 10 we would do
the following:

.. code-block:: python

 W = theano.shared ( W_values ) # we assume that ``W_values`` contains the
                                # initial values of your weight matrix

 bvis = theano.shared( bvis_values)
 bhid = theano.shared( bhid_values)

 trng = T.shared_randomstreams.RandomStreams(1234)

 def OneStep( vsample) :
    hmean   = T.nnet.sigmoid( theano.dot( vsample, W) + bhid)
    hsample = trng.binomial( size = hmean.shape, n = 1, prob = hmean)
    vmean   = T.nnet.sigmoid( theano.dot( hsample. W.T) + bvis)
    return trng.binomial( size = vsample.shape, n = 1, prob = vsample)

 sample = theano.tensor.vector()

 values, updates = theano.scan( OneStep, outputs_info = sample, n_steps = 10 )

 gibbs10 = theano.function([sample], values[-1], updates = updates)


Note that if we use shared variables ( ``W``, ``bvis``, ``bhid``) but
we do not iterate over them ( so scan doesn't really need to know
anything in particular about them, just that they are used inside the
function applied at each step) you do not need to pass them as
arguments. Scan will find them on its on and add them to the graph. Of
course, if you wish to (and it is good practice) you can add them, when
you call scan (they would be in the list of non sequence inputs).

The second, and probably most crucial observation is that the updates
dictionary becomes important in this case. It links a shared variable
with its updated value after k steps. In this case it tells how the
random streams get updated after 10 iterations. If you do not pass this
update dictionary to your function, you will always get the same 10
sets of random numbers. You can even use the ``updates`` dictionary
afterwards. Look at this example :

.. code-block:: python

 a = theano.shared(1)
 values,updates = theano.scan( lambda : {a:a+1}, n_steps = 10 )

In this case the lambda expression does not require any input parameters
and returns an update dictionary which tells how ``a`` should be updated
after each step of scan. If we write :

.. code-block:: python

  b = a+1
  c = updates[a] + 1
  f = theano.function([], [b,c], updates = updates)

  print b
  print c
  print a.value

We will see that because ``b`` does not use the updated version of
``a``, it will be 2, ``c`` will be 12, while ``a.value`` is ``11``.
If we call the function again, ``b`` will become 12, ``c`` will be 22
and ``a.value`` 21.

If we do not pass the ``updates`` dictionary to the function, then
``a.value`` will always remain 1, ``b`` will always be 2 and ``c``
will always be ``12``.



Reference
=========

.. automodule:: theano.scan

.. autofunction:: theano.map
.. autofunction:: theano.reduce
.. autofunction:: theano.foldl
.. autofunction:: theano.foldr
.. autofunction:: theano.scan
    :noindex:

