dA.txt

.. _daa:

Denoising Autoencoders (dA)
===========================

.. note::
  This section assumes the reader has already read through :doc:`logreg`
  and :doc:`mlp`. Additionally it uses the following Theano functions
  and concepts: `T.tanh`_, `shared variables`_, `basic arithmetic ops`_, `T.grad`_, `Random numbers`_, `floatX`_. If you intend to run the code on GPU also read `GPU`_.

.. _T.tanh: http://deeplearning.net/software/theano/tutorial/examples.html?highlight=tanh

.. _shared variables: http://deeplearning.net/software/theano/tutorial/examples.html#using-shared-variables

.. _basic arithmetic ops: http://deeplearning.net/software/theano/tutorial/adding.html#adding-two-scalars

.. _T.grad: http://deeplearning.net/software/theano/tutorial/examples.html#computing-gradients

.. _floatX: http://deeplearning.net/software/theano/library/config.html#config.floatX

.. _GPU: http://deeplearning.net/software/theano/tutorial/using_gpu.html

.. _Random numbers: http://deeplearning.net/software/theano/tutorial/examples.html#using-random-numbers


.. note::
    The code for this section is available for download `here`_.

.. _here: http://deeplearning.net/tutorial/code/dA.py


The Denoising Autoencoder (dA) is an extension of a classical
autoencoder and it was introduced as a building block for deep networks
in [Vincent08]_. We will start the tutorial with a short discussion on
:ref:`autoencoders`.

.. _autoencoders:

Autoencoders
+++++++++++++

See section 4.6 of [Bengio09]_ for an overview of auto-encoders.
An autoencoder takes an input :math:`\mathbf{x} \in [0,1]^d` and first
maps it (with an *encoder)* to a hidden representation :math:`\mathbf{y} \in [0,1]^{d'}`
through a deterministic mapping, e.g.:

.. math::

  \mathbf{y} = s(\mathbf{W}\mathbf{x} + \mathbf{b})

Where :math:`s` is a non-linearity such as the sigmoid. The latent
representation :math:`\mathbf{y}`, or **code** is then mapped back (with a
*decoder)* into a **reconstruction** :math:`\mathbf{z}` of the same shape as
:math:`\mathbf{x}`. The mapping happens through a similar transformation, e.g.:

.. math::

  \mathbf{z} = s(\mathbf{W'}\mathbf{y} + \mathbf{b'})

(Here, the prime symbol does not indicate matrix transposition.)
:math:`\mathbf{z}` should be seen as a prediction of :math:`\mathbf{x}`, given
the code :math:`\mathbf{y}`. Optionally, the weight matrix :math:`\mathbf{W'}`
of the reverse mapping may be constrained to be the transpose of the forward
mapping: :math:`\mathbf{W'} = \mathbf{W}^T`. This is referred to as *tied
weights*. The parameters of this model (namely :math:`\mathbf{W}`,
:math:`\mathbf{b}`, :math:`\mathbf{b'}` and, if one doesn't use tied weights,
also :math:`\mathbf{W'}`) are optimized such that the average reconstruction
error is minimized.

The reconstruction error can be measured in many ways, depending on the
appropriate distributional assumptions on the input given the code. The
traditional *squared error* :math:`L(\mathbf{x} \mathbf{z}) = || \mathbf{x} -
\mathbf{z} ||^2`, can be used. If the input is interpreted as either bit
vectors or vectors of bit probabilities, *cross-entropy* of the reconstruction
can be used:

.. math::

  L_{H} (\mathbf{x}, \mathbf{z}) = - \sum^d_{k=1}[\mathbf{x}_k \log
          \mathbf{z}_k + (1 - \mathbf{x}_k)\log(1 - \mathbf{z}_k)]

The hope is that the code :math:`\mathbf{y}` is a *distributed* representation
that captures the coordinates along the main factors of variation in the data.
This is similar to the way the projection on principal components would capture
the main factors of variation in the data. Indeed, if there is one linear
hidden layer (the *code)* and the mean squared error criterion is used to train
the network, then the :math:`k` hidden units learn to project the input in the
span of the first :math:`k` principal components of the data. If the hidden
layer is non-linear, the auto-encoder behaves differently from PCA, with the
ability to capture multi-modal aspects of the input distribution. The departure
from PCA becomes even more important when we consider *stacking multiple
encoders* (and their corresponding decoders) when building a deep auto-encoder
[Hinton06]_.

Because :math:`\mathbf{y}` is viewed as a lossy compression of
:math:`\mathbf{x}`, it cannot be a good (small-loss) compression for all
:math:`\mathbf{x}`. Optimization makes it a good compression for training
examples, and hopefully for other inputs as well, but not for arbitrary inputs.
That is the sense in which an auto-encoder generalizes: it gives low
reconstruction error on test examples from the same distribution as the
training examples, but generally high reconstruction error on samples randomly
chosen from the input space.

We want to implement an auto-encoder using Theano, in the form of a class, that
could be afterwards used in constructing a stacked autoencoder. The first step
is to create shared variables for the parameters of the autoencoder
:math:`\mathbf{W}`, :math:`\mathbf{b}` and :math:`\mathbf{b'}`. (Since we are
using tied weights in this tutorial, :math:`\mathbf{W}^T` will be used for
:math:`\mathbf{W'}`):

.. literalinclude:: ../code/dA.py
  :pyobject: dA.__init__

Note that we pass the symbolic ``input`` to the autoencoder as a parameter.
This is so that we can concatenate layers of autoencoders to form a deep
network: the symbolic output (the :math:`\mathbf{y}` above) of layer :math:`k` will
be the symbolic input of layer :math:`k+1`.

Now we can express the computation of the latent representation and of the reconstructed
signal:

.. literalinclude:: ../code/dA.py
  :pyobject: dA.get_hidden_values

.. literalinclude:: ../code/dA.py
  :pyobject: dA.get_reconstructed_input

And using these functions we can compute the cost and the updates of
one stochastic gradient descent step:

.. literalinclude:: ../code/dA.py
  :pyobject: dA.get_cost_updates

We can now define a function that applied iteratively will update the
parameters ``W``, ``b`` and ``b_prime`` such that the
reconstruction cost is approximately minimized.

.. literalinclude:: ../code/dA.py
  :start-after: theano_rng = RandomStreams(rng.randint(2 ** 30))
  :end-before: start_time = time.clock()

If there is no constraint besides minimizing the reconstruction error, one
might expect an auto-encoder with :math:`n` inputs and an encoding of dimension
:math:`n` (or greater) to learn the identity function, merely mapping an input
to its copy. Such an autoencoder would not differentiate test examples (from
the training distribution) from other input configurations. 

Surprisingly,
experiments reported in [Bengio07]_ suggest that, in practice, when trained
with stochastic gradient descent, non-linear auto-encoders with more hidden
units than inputs (called overcomplete) yield useful representations. (Here,
"useful" means that a network taking the encoding as input has low
classification error.) 

A simple explanation is that stochastic gradient descent with early stopping is
similar to an L2 regularization of the parameters. To achieve perfect
reconstruction of continuous inputs, a one-hidden layer auto-encoder with
non-linear hidden units (exactly like in the above code) needs very small
weights in the first (encoding) layer, to bring the non-linearity of the hidden
units into their linear regime, and very large weights in the second (decoding)
layer. With binary inputs, very large weights are also needed to completely
minimize the reconstruction error. Since the implicit or explicit
regularization makes it difficult to reach large-weight solutions, the
optimization algorithm finds encodings which only work well for examples
similar to those in the training set, which is what we want. It means that the
*representation is exploiting statistical regularities present in the training
set,* rather than merely learning to replicate the input.

There are other ways by which an auto-encoder with more hidden units than inputs
could be prevented from learning the identity function, capturing something
useful about the input in its hidden representation. One is the addition of
*sparsity* (forcing many of the hidden units to be zero or near-zero). Sparsity
has been exploited very successfully by many [Ranzato07]_ [Lee08]_. Another is
to add randomness in the transformation from input to reconstruction. This
technique is used in Restricted Boltzmann Machines (discussed later in
:ref:`rbm`), as well as in Denoising Auto-Encoders, discussed below.

.. _DA:

Denoising Autoencoders
++++++++++++++++++++++

The idea behind denoising autoencoders is simple. In order to force
the hidden layer to discover more robust features and prevent it
from simply learning the identity, we train the
autoencoder to *reconstruct the input from a corrupted version of it*.

The denoising auto-encoder is a stochastic version of the auto-encoder.
Intuitively, a denoising auto-encoder does two things: try to encode the input
(preserve the information about the input), and try to undo the effect of a
corruption process stochastically applied to the input of the auto-encoder. The
latter can only be done by capturing the statistical dependencies between the
inputs. The denoising auto-encoder can be understood from different
perspectives (the manifold learning perspective, stochastic operator
perspective, bottom-up -- information theoretic perspective, top-down --
generative model perspective), all of which are explained in [Vincent08]_. See
also section 7.2 of [Bengio09]_ for an overview of auto-encoders.

In [Vincent08]_, the stochastic corruption process randomly sets some of the
inputs (as many as half of them) to zero. Hence the denoising auto-encoder is
trying to *predict the corrupted (i.e. missing) values from the uncorrupted
(i.e., non-missing) values*, for randomly selected subsets of missing patterns.
Note how being able to predict any subset of variables from the rest is a
sufficient condition for completely capturing the joint distribution between a
set of variables (this is how Gibbs sampling works).

To convert the autoencoder class into a denoising autoencoder class, all we
need to do is to add a stochastic corruption step operating on the input. The input can be
corrupted in many ways, but in this tutorial we will stick to the original
corruption mechanism of randomly masking entries of the input by making
them zero. The code below
does just that:

.. literalinclude:: ../code/dA.py
  :pyobject: dA.get_corrupted_input


In the stacked autoencoder class (:ref:`stacked_autoencoders`) the weights of
the ``dA`` class have to be shared with those of a corresponding sigmoid layer.
For this reason, the constructor of the ``dA`` also gets Theano variables
pointing to the shared parameters. If those parameters are left to ``None``,
new ones will be constructed.

The final denoising autoencoder class becomes:

.. literalinclude:: ../code/dA.py
  :pyobject: dA



Putting it All Together
+++++++++++++++++++++++


It is easy now to construct an instance of our ``dA`` class and train
it.

.. literalinclude:: ../code/dA.py
  :language: python
  :start-after: start-snippet-2
  :end-before: end-snippet-2

.. literalinclude:: ../code/dA.py
  :start-after: start-snippet-3
  :end-before: end-snippet-3


In order to get a feeling of what the network learned we are going to
plot the filters (defined by the weight matrix). Bear in mind, however,
that this does not provide the entire story,
since we neglect the biases and plot the weights up to a multiplicative
constant (weights are converted to values between 0 and 1).

To plot our filters we will need the help of ``tile_raster_images`` (see
:ref:`how-to-plot`) so we urge the reader to study it. Also
using the help of the Python Image Library, the following lines of code will
save the filters as an image:

.. literalinclude:: ../code/dA.py
  :start-after: start-snippet-4
  :end-before: end-snippet-4


Running the Code
++++++++++++++++

To run the code:

.. code-block:: bash

  python dA.py

The resulted filters when we do not use any noise are:

.. figure:: images/filters_corruption_0.png
    :align: center



The filters for 30 percent noise:


.. figure:: images/filters_corruption_30.png
    :align: center