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<section id="neural-network-models-unsupervised">
<span id="neural-networks-unsupervised"></span><h1><span class="section-number">2.9. </span>Neural network models (unsupervised)<a class="headerlink" href="#neural-network-models-unsupervised" title="Link to this heading">#</a></h1>
<section id="restricted-boltzmann-machines">
<span id="rbm"></span><h2><span class="section-number">2.9.1. </span>Restricted Boltzmann machines<a class="headerlink" href="#restricted-boltzmann-machines" title="Link to this heading">#</a></h2>
<p>Restricted Boltzmann machines (RBM) are unsupervised nonlinear feature learners
based on a probabilistic model. The features extracted by an RBM or a hierarchy
of RBMs often give good results when fed into a linear classifier such as a
linear SVM or a perceptron.</p>
<p>The model makes assumptions regarding the distribution of inputs. At the moment,
scikit-learn only provides <a class="reference internal" href="generated/sklearn.neural_network.BernoulliRBM.html#sklearn.neural_network.BernoulliRBM" title="sklearn.neural_network.BernoulliRBM"><code class="xref py py-class docutils literal notranslate"><span class="pre">BernoulliRBM</span></code></a>, which assumes the inputs are
either binary values or values between 0 and 1, each encoding the probability
that the specific feature would be turned on.</p>
<p>The RBM tries to maximize the likelihood of the data using a particular
graphical model. The parameter learning algorithm used (<a class="reference internal" href="#sml"><span class="std std-ref">Stochastic
Maximum Likelihood</span></a>) prevents the representations from straying far
from the input data, which makes them capture interesting regularities, but
makes the model less useful for small datasets, and usually not useful for
density estimation.</p>
<p>The method gained popularity for initializing deep neural networks with the
weights of independent RBMs. This method is known as unsupervised pre-training.</p>
<figure class="align-center">
<a class="reference external image-reference" href="../auto_examples/neural_networks/plot_rbm_logistic_classification.html"><img alt="../_images/sphx_glr_plot_rbm_logistic_classification_001.png" src="../_images/sphx_glr_plot_rbm_logistic_classification_001.png" style="width: 420.0px; height: 400.0px;" />
</a>
</figure>
<p class="rubric">Examples</p>
<ul class="simple">
<li><p><a class="reference internal" href="../auto_examples/neural_networks/plot_rbm_logistic_classification.html#sphx-glr-auto-examples-neural-networks-plot-rbm-logistic-classification-py"><span class="std std-ref">Restricted Boltzmann Machine features for digit classification</span></a></p></li>
</ul>
<section id="graphical-model-and-parametrization">
<h3><span class="section-number">2.9.1.1. </span>Graphical model and parametrization<a class="headerlink" href="#graphical-model-and-parametrization" title="Link to this heading">#</a></h3>
<p>The graphical model of an RBM is a fully-connected bipartite graph.</p>
<img alt="../_images/rbm_graph.png" class="align-center" src="../_images/rbm_graph.png" />
<p>The nodes are random variables whose states depend on the state of the other
nodes they are connected to. The model is therefore parameterized by the
weights of the connections, as well as one intercept (bias) term for each
visible and hidden unit, omitted from the image for simplicity.</p>
<p>The energy function measures the quality of a joint assignment:</p>
<div class="math notranslate nohighlight">
\[E(\mathbf{v}, \mathbf{h}) = -\sum_i \sum_j w_{ij}v_ih_j - \sum_i b_iv_i
- \sum_j c_jh_j\]</div>
<p>In the formula above, <span class="math notranslate nohighlight">\(\mathbf{b}\)</span> and <span class="math notranslate nohighlight">\(\mathbf{c}\)</span> are the
intercept vectors for the visible and hidden layers, respectively. The
joint probability of the model is defined in terms of the energy:</p>
<div class="math notranslate nohighlight">
\[P(\mathbf{v}, \mathbf{h}) = \frac{e^{-E(\mathbf{v}, \mathbf{h})}}{Z}\]</div>
<p>The word <em>restricted</em> refers to the bipartite structure of the model, which
prohibits direct interaction between hidden units, or between visible units.
This means that the following conditional independencies are assumed:</p>
<div class="math notranslate nohighlight">
\[\begin{split}h_i \bot h_j | \mathbf{v} \\
v_i \bot v_j | \mathbf{h}\end{split}\]</div>
<p>The bipartite structure allows for the use of efficient block Gibbs sampling for
inference.</p>
</section>
<section id="bernoulli-restricted-boltzmann-machines">
<h3><span class="section-number">2.9.1.2. </span>Bernoulli Restricted Boltzmann machines<a class="headerlink" href="#bernoulli-restricted-boltzmann-machines" title="Link to this heading">#</a></h3>
<p>In the <a class="reference internal" href="generated/sklearn.neural_network.BernoulliRBM.html#sklearn.neural_network.BernoulliRBM" title="sklearn.neural_network.BernoulliRBM"><code class="xref py py-class docutils literal notranslate"><span class="pre">BernoulliRBM</span></code></a>, all units are binary stochastic units. This
means that the input data should either be binary, or real-valued between 0 and
1 signifying the probability that the visible unit would turn on or off. This
is a good model for character recognition, where the interest is on which
pixels are active and which aren’t. For images of natural scenes it no longer
fits because of background, depth and the tendency of neighbouring pixels to
take the same values.</p>
<p>The conditional probability distribution of each unit is given by the
logistic sigmoid activation function of the input it receives:</p>
<div class="math notranslate nohighlight">
\[\begin{split}P(v_i=1|\mathbf{h}) = \sigma(\sum_j w_{ij}h_j + b_i) \\
P(h_i=1|\mathbf{v}) = \sigma(\sum_i w_{ij}v_i + c_j)\end{split}\]</div>
<p>where <span class="math notranslate nohighlight">\(\sigma\)</span> is the logistic sigmoid function:</p>
<div class="math notranslate nohighlight">
\[\sigma(x) = \frac{1}{1 + e^{-x}}\]</div>
</section>
<section id="stochastic-maximum-likelihood-learning">
<span id="sml"></span><h3><span class="section-number">2.9.1.3. </span>Stochastic Maximum Likelihood learning<a class="headerlink" href="#stochastic-maximum-likelihood-learning" title="Link to this heading">#</a></h3>
<p>The training algorithm implemented in <a class="reference internal" href="generated/sklearn.neural_network.BernoulliRBM.html#sklearn.neural_network.BernoulliRBM" title="sklearn.neural_network.BernoulliRBM"><code class="xref py py-class docutils literal notranslate"><span class="pre">BernoulliRBM</span></code></a> is known as
Stochastic Maximum Likelihood (SML) or Persistent Contrastive Divergence
(PCD). Optimizing maximum likelihood directly is infeasible because of
the form of the data likelihood:</p>
<div class="math notranslate nohighlight">
\[\log P(v) = \log \sum_h e^{-E(v, h)} - \log \sum_{x, y} e^{-E(x, y)}\]</div>
<p>For simplicity the equation above is written for a single training example.
The gradient with respect to the weights is formed of two terms corresponding to
the ones above. They are usually known as the positive gradient and the negative
gradient, because of their respective signs. In this implementation, the
gradients are estimated over mini-batches of samples.</p>
<p>In maximizing the log-likelihood, the positive gradient makes the model prefer
hidden states that are compatible with the observed training data. Because of
the bipartite structure of RBMs, it can be computed efficiently. The
negative gradient, however, is intractable. Its goal is to lower the energy of
joint states that the model prefers, therefore making it stay true to the data.
It can be approximated by Markov chain Monte Carlo using block Gibbs sampling by
iteratively sampling each of <span class="math notranslate nohighlight">\(v\)</span> and <span class="math notranslate nohighlight">\(h\)</span> given the other, until the
chain mixes. Samples generated in this way are sometimes referred as fantasy
particles. This is inefficient and it is difficult to determine whether the
Markov chain mixes.</p>
<p>The Contrastive Divergence method suggests to stop the chain after a small
number of iterations, <span class="math notranslate nohighlight">\(k\)</span>, usually even 1. This method is fast and has
low variance, but the samples are far from the model distribution.</p>
<p>Persistent Contrastive Divergence addresses this. Instead of starting a new
chain each time the gradient is needed, and performing only one Gibbs sampling
step, in PCD we keep a number of chains (fantasy particles) that are updated
<span class="math notranslate nohighlight">\(k\)</span> Gibbs steps after each weight update. This allows the particles to
explore the space more thoroughly.</p>
<p class="rubric">References</p>
<ul class="simple">
<li><p><a class="reference external" href="https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf">“A fast learning algorithm for deep belief nets”</a>,
G. Hinton, S. Osindero, Y.-W. Teh, 2006</p></li>
<li><p><a class="reference external" href="https://www.cs.toronto.edu/~tijmen/pcd/pcd.pdf">“Training Restricted Boltzmann Machines using Approximations to
the Likelihood Gradient”</a>,
T. Tieleman, 2008</p></li>
</ul>
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