## The EM algorithm and Gaussian mixture models – part I

In the last few posts on machine learning, we have looked in detail at restricted Boltzmann machines. RBMs are a prime example for unsupervised learning – they learn a given distribution and are able to extract features from a data set, without the need to label the data upfront.

However, there are of course many other, by now almost classical, algorithms for unsupervised learning. In this and the following posts, I will explain one of the most commonly applied algorithms in this field, namely the EM algorithm.

As a preparation, let us start with a very fundamental exercise – clustering. So suppose that we are given a data set which we suspect to consist of K clusters. We want to identify these clusters, i.e. to each point in the dataset, we want to assign the number of the cluster to which the point belongs.

More precisely, let us suppose that we are given a set ${\mathcal D} = \{ x_i, \dots, x_N\}$ of points in some euclidian space and a number K of clusters. We want to identify the centre $\mu_i$ of each cluster and then assign the data points xi to some of the $\mu_j$, namely to the $\mu_j$ which is closest to xi. We can encode the assignment of data points to clusters in a matrix Rij where Rij = 1 if data point xi belongs to cluster j with centre $\mu_j$. Thus each row of R corresponds to a data point and is non-zero in exactly one column, where it is one. This is known as 1-of-K encoding.

For each value of i, the squared distance of xi to the centre of the cluster to which it is assigned is then given by

$\sum_j R_{ij} \| x_i - \mu_j |^2$

where we note that only one of the summands is different from zero. Assigning the data points to the clusters and positioning the centre points of the clusters then amounts to minimizing the loss function

$l(\mu, R) = \sum_i \sum_j R_{ij} \langle x_i - \mu_j , x_i - \mu_j \rangle$

where the brackets denote the usual euclidean scalar product.

Now let us see how we can optimize this function. For a fixed vector $\mu$ of cluster centers, we can easily minimize Rij by assigning each data point to the cluster whose center is closest to xi. Thus we set

$R_{ij} = \begin{cases} 1 & \text{if} \, j = \arg \min \| x_i - \mu_j \|^2 \\ 0 & \text{otherwise} \end{cases}$

Conversely, given a matrix R which we hold fixed, it is likewise easy to minimize $\mu_j$. As there are no further constraints on $\mu_j$, we can find the minimum by differentiating the loss function. We find that the derivative is zero if and only if

$0 = \sum_i R_{ij}(x_i - \mu_j)$

holds for all j. Assuming for a moment that each cluster has at least one data point assigned to it, i.e. that none of the columns of R contains zeroes only, we can solve this by

$\mu_j = \frac{\sum_i x_i R_{ij}}{\sum_i R_{ij}}$

which is also easily seen to be a local minimum by taking the second derivative.

Note that this term has an obvious geometric interpretation. The denominator in this expression is the number of data points that are currently assigned to cluster j. The numerator is the sum of all data points assigned to this cluster. Thus the minimum is the mean value of the positions of the data points that are currently assigned to the cluster (a physicist would call this the center of gravity of the cluster). This is the reason why this method is called the k-means algorithm. If no data points are assigned to the cluster, the loss function does not depend on $\mu_j$ and we can choose any value that we want.

The algorithm now works as follows. First, we choose centers $\mu_j$ at will – it is common to use some of the data points for this purpose. Then we assign each data point to a center using the formula for Rij derived above. We then adjust the center points $\mu_j$ and reallocate the points to the new centers and so forth.

From our discussion above, it is clear that each full iteration of this procedure will reduce the loss function. This does of course not imply that the algorithm converges to a global minimum, as it might get stuck in local minima or saddle points. In practice, the algorithm is executed until the cluster assignments and centers do not change any more substantially or for a given number of steps.

The diagram above shows the result of applying this algorithm to a set of points that is organized in two clusters. To generate the data, 100 samples were drawn from 2-dimensional Gaussian distributions. On the left hand side, half of the the samples were centered at the point (5,1), the other samples at (1,4), and both had standard deviation 0.6. On the right hand side, the same centers were used, but only a small number of samples were drawn from the second distribution which had standard deviation 0.5, whereas most samples came from the first distribution with standard deviation 0.8. Then 10 iterations of the k-means algorithm were applied to the resulting sample set. The points in the sample were then plotted with a color indicating the assignment to clusters resulting from the matrix R. The actual cluster from which the sample was drawn is indicated by the shape – a point is cluster one, a diamond is cluster two.

We see that in the example on the left hand side, the algorithm has correctly assigned all points to their original cluster. For the example on the right hand side, the situation is different – the algorithm did actually assign significantly more points to the blue cluster, i.e. there are many wrong assignments (blue points). This does not change substantially if we increase the number of iterations, even with 100 iterations, we still see many wrong assigments for this example. If you want to reproduce the example and play a bit with the parameters, you can get the sourcecode for a straightforward implementation in Python from my GitHub repository.

The K-means algorithm is very simple and straightforward, but seems to have limitations because it cannot determine the shape of a distribution, only its center. It turns out that K-means is a special case of a broader class of algorithms that we now study, hoping to find more robust algorithms.

In our example, we have generated sample data as a combination of two Gaussian distributions. What if we just change the game and simply assume that our data is of this type? In other words, we assume an underlying probabilistic model for our sample data. Once we have that, we can pull all the tricks that statistical inference can offer – we can calculate maximum likelihood and maximum posterior probability, we can try to determine the posterior or even sample from the model.

Thus let us try to fit our data to a model of the form

$P(x) = \sum_k \pi_k {\mathcal N}(x ; \mu_k, \Sigma_k)$

where ${\mathcal N}(x ; \mu_k, \Sigma_k)$ is a multivariate Gaussian distribution with mean $\mu_k$ and covariance matrix $\Sigma_k$, i.e.

${\mathcal N}(x ; \mu_k, \Sigma_k) = \frac{1}{\sqrt{(2\pi)^d \det(\Sigma)}} \exp (-\frac{1}{2} \langle x - \mu_k, \Sigma_k^{-1}(x - \mu_k)\rangle)$

and the $\pi_k$ are non-negative real numbers with $\sum_k \pi_k = 1$.

Let us now see how this equation looks like if we use a 1-of-K encoding. We introduce a random variable Z that takes values in $\{ 0, 1\}^K$ with the additional constraint that only one of the Zk is allowed to be different from zero. We interpret $\pi_k$ as the probability

$\pi_k = P(Z_k = 1)$

Then

$P(Z = z) = \prod_k \pi_k ^{z_k}$

and we can write

$P(X=x) = \sum_z P(Z=z) P(X=x | Z=z) = \sum_z P(x,z)$

where $P(z)$ is as above and

$P(X = x | Z = z) = \prod_k {\mathcal N}(x ; \mu_k, \Sigma_k)^{z_k}$

This is a very general type of distribution which reflects a common pattern in machine learning, namely the introduction of so called latent or hidden variables. In general, latent or hidden variables are random variables that are a part of the model which cannot be observed, i.e. are not part of the input or the output of the model. We have seen latent variables in action several times – adding hidden units to a neural network introduces latent variables and makes the model much more powerful, the hidden layer of a restricted Boltzmann machine serves as memory to learn features, and latent variables that are used to construct a mixture of Gaussians as above allow us to model a much broader class of distributions than a model with just one Gaussian.

Intuitively, it is also clear how to sample from such a model. In a first step, we sample from Z, in other words we determine the index k randomly according to the distribution given by the $\pi_k$. Once we have k, we then sample from the conditional distribution $P(X = x | Z = z)$. As we already have k, this amounts to sampling from the Gaussian distribution ${\mathcal N}(x ; \mu_k, \Sigma_k)$ with mean $\mu_k$ and covariance matrix $\Sigma_k$.

In the example above, we have first applied this procedure to a one-dimensional Gaussian mixture with K=2. The histogram shows the result of the sampling procedure, the solid lines are the probability density functions of the two individual Gaussian distributions, multiplied by the respective weight. On the right hand side, the result of sampling a Gaussian mixture in two dimensions is displayed (you can download the notebook used to produce this example here).

When we now try to fit this model to the sample data, we again have to calculate the likelihood function and try to maximize it. However, it turns out that the gradient of the likelihood is not so easy to calculate, and it might very well be that there is no closed form solution or that we obtain rather complicated expressions for the gradient. Fortunately, there is an alternative that works for very general models and does not require knowledge of the gradient – the EM algorithm. In the next post, I will present the algorithm in this general setup, before we apply it to our original problem and compare the results with the k-means algorithm.

### References

1. C.M. Bishop, Pattern recognition and machine learning, Springer, New York 2006
2. A.P. Dempster, N.M. Laird, D.B. Rubin, Maximum likelihood from incomplete data via the EM-algorithm, Journ. Royal Stat. Soc. Series B. Vol. 39 No. 1 (1977), pp. 1-38

## Why you need statistics to understand neuronal networks

When I tried to learn about neuronal networks first, I did what probably most of us would do – I started to look for tutorials, blogs etc. on the web and was surprised by the vast amount of resources that I found. Almost every blog or webpage about neuronal networks has a section on training a simple neuronal network, maybe on the MNIST data set, using a framework like TensorFlow, Theano or MXNET. When you follow such a tutorial, a network is presented as a collection of units and weights. You see how the output of the network is calculated and then an error function – sometimes least squares, sometimes something else – is presented. Often, a regulation term is applied, and then you are being told that the automatic gradient calculation features of the framework will do the gradient descent algorithm for you and you just have to decide on an optimizer and run the network and enjoy the results.

Sooner or later, however, you will maybe start to ask a few questions. Why that particular choice of the loss function? Where does the regulator come from? What is a good initial value for the weights and why? Where does the sigmoid function come from? And many, many other questions….

If you then decide to dig deeper, using one of the many excellent textbooks or even try to read some of the original research papers (and some are actually quite readable), you will very soon be confronted with terms like entropy, maximum likelihood, posterior distribution, Gaussian mixtures and so on, and you will realize that the mathematics of neuronal networks has a strong overlap with mathematical statistics. But why? Why is that a good language to discuss neuronal networks, and why should you take the time to refresh your statistics knowledge if you really want to understand neuronal networks? In this post, I will try to argue that statistical inference comes up very naturally when you try to study neuronal networks.

Many neuronal networks are designed to excel at classification tasks. As an example, suppose you wanted to design and train a neuronal network that, given data about an animal, classifies the animal as either a bird or some other animal (called a “non-bird” for convenience). So our starting point is a set modelling all possible objects that could be presented to the network. How exactly we model this set is not so important, more important is that in general, the network will not have access to all the data about the animal, but only to certain attributes of elements in the set called features. So there could be a feature which we call X1 and which is defined as

$X_1 = \text{the animal can fly}$

taking values in $\{0,1\}$. Another data point the network could get is

$X_2 = \text{length of animal in cm}$

taking values in the reals and so forth. More generally, we assume that on the set of all possible objects, we have certain functions Xi taking values in, say, the real numbers. Based on these numbers, the network will then try to take a decision whether a given animal is a bird or not. Thus we do not have to deal directly with our space of objects, but use the functions Xi as primary objects.

If the network had a chance to look at every possible animal, this would be easy, even though it would cost a lot memory – it could simply remember all possible combinations of features and for each feature, store the correct answer. In reality however, this does not work. Instead, we have access to a small subset of the data – a sample – for which we can evaluate the Xi. Based on this subset, we then have to derive a model which gives the right answer in as many cases as possible. Thus we try to make a statement about the full space of things that could be presented to our network for classification based on a small sample.

This is where probabilities come into play very naturally. We need to assume that our sample has been chosen randomly, but still we need to make assertions about the full set. This is exactly what inferential statistics is doing. The fact that our sample is chosen randomly turns our Xi into random variables. Similarly, the variable

$Y = \text{is a bird}$

taking values in $\{0,1\}$ is a random variable, and we try to gain information on the distribution of Y across the full population based on its values on a given set of labelled samples, i.e. a set of samples where Y is known. Thus Y would represent the labels or targets in the language of neuronal networks. Applying the methods of statistical inference to this situation would typically start by choosing a statistical model and than using estimators or hypothesis testing to make deductions.

Apart from the fact that we have to derive information on the full population based on a sample, there is another reason why probabilities appear naturally in the theory of machine learning. In many cases, the available input – being a reduction of the full set of data – is not sufficient to classify the sample with full certainty. To see this, let us go back to our examples. How would you derive the property “bird” from the given data “can fly” and “length”? Not all animals than can fly are birds – and not all birds can fly. So we have to try to distinguish for instance a butterfly from a hummingbird based on the length. The smallest hummingbird – a bee hummingbird – is about 5 cm in length. The largest known butterfly – the Queen’s Alexandra birdwing – can be as long as 8 cm (both informations taken from Wikipedia). Thus our data is not sufficient to clearly distinguish butterflies and birds in all cases!

However, very small birds and very large butterflies have one thing in common – they are rare. So chances are that a given animal that can fly and is larger than 5 cm is actually a bird (yes, I know, there are bats….). In other words, if again Y denotes the variable which is 1 on birds and 0 on all other animals, we can in general not hope that Y is a function of the Xi, but we can hope that given some values of the Xi, the probability $P(Y=1)$ to be a bird is a function of the Xi. In other words, using the language of conditional probabilities,

$P(Y=1 | X = x) = f(x)$

with some unknown function f. In a Bayesian interpretation of probability, the certainty with which can say “this animal is a bird” is a function of the values xi of the observable variables Xi.

With these considerations, we now arrive at the following mathematical model for what a classification algorithm is about. We are given a probability space $(P, \Omega)$ with a vector valued random variable X. The attributes of a sample are described by the feature vector X in some subset of m-dimensional euclidian space, where m is the number of different features. In our example, m=2, as we try to classify animals based on two properties. The result of the classification is described by a random variable Y taking – for the simple case of a binary classification problem – values in $\{0,1\}$. We then assume that

$P(Y =1 | X=x) = f(x;w_0)$

where $f(\cdot;w)$ is a function parametrized by some parameter w that we call the weights of the model. The actual value w0 of w is unknown. Based on a sample for X and Y, we then try to fit the model, i.e. we try to find a value for w such that $f(\cdot, w)$ models the actual conditional distribution of Y as good as possible. Once the fitting phase is completed, we can then use the model to derive predictions about objects which are not in our initial sample set.

This model sounds a bit abstract, but many feed forward neuronal networks can be described with this or similar models. And we can now apply the full machinery of mathematical statistics – we can calculate cross entropies and maximum likelihood, we can analyse converge and variance, we can apply the framework of Bayesian statistics and Monte Carlo methods. This is the reason why statistics is so essential when it comes to describing and analyzing neuronal networks. So on the next rainy Sunday afternoon, you might want to grab a steaming hot cup of coffee, head towards your arm chair and spent some time with one of the many good exposures on this topic, like chapter IV in MacKays book on Machine Learning, or Bishops “Pattern recognition and machine learning” or chapter 3 of the deep learning book by Goodfellow, Bengio and Courville.

## Training a restricted Boltzmann machine on a GPU with TensorFlow

During the second half of the last decade, researchers have started to exploit the impressive capabilities of graphical processing units (GPUs) to speed up the execution of various machine learning algorithms (see for instance [1] and [2] and the references therein). Compared to a standard CPU, modern GPUs offer a breathtaking degree of parallelization – one of NVIDIAs current flagships, the Tesla V100, offers more than 5.000 CUDA cores that can perform work in parallel. As training and evaluating neural networks involves many floating operations on large matrices, they can benefit heavily from the special capabilities that a GPU provides.

So how can we make our code execute on a GPU? Of course you could program directly against the CUDA interface or similar interfaces like OpenCL. But specifically for the purposes of machine learning, there are easier options – over the last years, several open source frameworks like Theano, Torch, MXNet or TensorFlow have become available that make it comparatively easy to leverage a GPU for machine learning. In this post, I will use the TensorFlow framework, simply because so far this is the only one of these frameworks that I have used (though MXNet looks very interesting too and I might try that out and create a post on it at some point in the future).

It takes some time to get used to the programming model of TensorFlow which is radically different from the usual imparative programming style. As an example, let us suppose we wanted to add two matrices. In Python, using numpy, this would look as follows.

import numpy as np
a = np.matrix([[0, 1], [1, 0]])
b = np.matrix([[1, 0], [0, 1]])
c = a + b
print(c)


This program is described by a sequence of instructions (let us ignore the fact for a moment that these are of course functions that we call – ultimately, functions are composed of instructions). When we execute this program, the instructions are processed one by one. First, we assign a value to the variable a, then we assign a value to a variable b, then we add these two values and assign the result to a variable c and finally we print out the value of c.

The programming model behind TensorFlow (and other frameworks like Theano) is fundamentally different. Instead of describing a program as a sequence of instructions, the calculations are organized as a graph. The nodes in this graph correspond to operations. The edges joining the nodes represent the flow of data between the operations. In TensorFlow, data is always represented as a tensor, so the edges in the graph are tensors. An operation consumes data from its inputs, processes it and forwards it to the next operation in the graph as its output.

A program using TensorFlow typically consists of two phases. In the first phase, we build the graph, i.e. we define the operations and their inputs and outputs that make up the calculation that we want to perform. However, in this phase, no calculations are actually performed. Instead, this happens in the second phase when we actually run the graph. For that purpose, we create a session. Roughly speaking, a session defines an environment in which a graph can be executed. Once the session has been defined, we can invoke its run method. To the run method, we pass as an argument the operation in the graph that we want to execute. The run method will then trace the graph backwards and evaluate all operations that provide input to our target operation recursively, i.e. it will identify the subgraph that needs to be executed to evaluate our target operation.

Let us again use the example of a simple addition to illustrate this. The source code looks as follows.

import tensorflow as tf
#
# Build the model
#
a = tf.constant([[0, 1], [1, 0]], name="a")
b = tf.constant([[1, 0], [0, 1]], name="b")

#
# Create a session and run it
#
session = tf.Session()
print(session.run(c))


First, we import the tensorflow library itself. Then, in the next three lines, we build the graph. We define three nodes in the graph. The first two nodes are special operations that output simply a constant value. The third operation is the operation that performs the actual addition and uses the previously defined operations as input. Thus our final graph has three nodes and two edges, as shown below.h

In the next line, we create a TensorFlow session which we then run. The argument specifies which operation we want to execute and therefore determines which part of the graph we will actually run. The output of the run method is an ordinary numpy array which we then print out.

Let us now look at an example which is slightly more complicated. In the PCD algorithm, we can compute the contribution of the negative phase to the weight updates as follows.

E = expit(self.beta*(np.matmul(S0, self.W) + self.c))
pos = np.tensordot(S0, E, axes=((0),(0)))


Here S0 is a batch from the sample set, W is the current value of the weights and c is the current value of the bias. In TensorFlow, the code to build the corresponding part of the model looks quite similar.

S0 = tf.placeholder(name="S0", shape=[batch_size, self.visible],
dtype=tf.float32)
W = tf.get_variable(name="W",
dtype=tf.float32,
shape=[self.visible, self.hidden],
initializer = tf.zeros_initializer(),
trainable=False)
c = tf.get_variable(name="c",
dtype=tf.float32,
shape=[1, self.hidden],
initializer = tf.zeros_initializer(),
trainable=False)
E = tf.sigmoid(self.beta*(tf.matmul(S0, W) + c), name="E")
pos = tf.tensordot(S0, E, axes=[[0],[0]], name="pos")


The first element that we define – S0 – is a so called placeholder. This is a bit like a constant, with the difference that its value can be specified per run, using an additional argument called feed dictionary to the Session.run method. The next two elements that we define are variables. Variables are similar to operations – they represent nodes in the network and provide an output, but have no input. Instead, they have a certain value and feed that value as outputs to other operations. We then use the built-in tensorflow operations sigmoid and tensordot to calculate the expectation values of the visible units and the positive phase.

The full model to train a restricted Boltzmann machine is of course a bit more complicated. TensorFlow comes with a very useful device called TensorBoard that can be used to visualize a graph constructed in TensorFlow. The image below has been created using TensorFlow and shows the full graph of our restricted Boltzmann machine.

TensorBoard offers the option to combine operations into groups which are then collapsed in the visual representation. In the image above, all groups are collapsed except the group representing the contribution from the positive phase. We can clearly see the flow of data as described above – we first multiply S0 and W, then add c to the result, multiply this by a constant (the inverse temperature, called x in the diagram) and then apply the sigmoid operation that we have called E. The result is then fed into other, collapsed groups like the group delta which holds the part of the model responsible for calculating the weight updates.

I will not go through the full source code that you can find on GitHub as usual – you will probably find the well written tutorial on the TensorFlow homepage useful when going through this. Instead, let us play around a bit with the result.

As the PC that is under my desk is almost seven years old and does not have a modern GPU, I did use a p2.xlarge instance from Amazon EC2 which gave me access to a Tesla K80 GPU and four Intel Xeon E5-2686 cores running at 2.3 GHz (be careful – this instance type is not covered by the free usage tier, so that will cost you a few dollars). I used the Amazon provided Deep Learning AMI based on Ubuntu 16.04. After logging into the instance, we first have to complete a few preparational steps.

$source activate tensorflow_p36$ git clone http://www.github.com/christianb93/MachineLearning.git
$cd MachineLearning$ export MPLBACKEND="AGG"
$conda install scikit-learn$ python3 RBM.py --algorithm=PCDTF


Here we activate the pre-configured TensorFlow environment, download the source code from GitHub, set the environment variable to define our Matplotlib backend, and download and install some required packages. Then we do a first run with the BAS dataset to verify that everything works. If that is the case, we can run the actual MNIST training and sampling.

$python3 RBM.py --N=28 --data=MNIST --save=1 --hidden=128 --pattern=256 --batch_size=128 --epochs=40000 --run_samples=1 --sample_size=6,6 --beta=1.0 --sample=200000 --algorithm=PCDTF --precision=32  This produced the following sample of 6 x 6 digits. The execution took roughly 5 minutes – 2 minutes for the training phase and 3 minutes for the sampling phase. During the training, the GPU utilization (captured with nvidia-smi -l 2) was at around 57% and stayed in that range during the sampling phase. A second run using the switch --precision=64 to set the floating point precision to 64 bits did not substantially change the outcome or the performance. Then a run with the same parameters was done in pure Python running on the four CPU cores provided by the p2.xlarge instance (--algorithm=PCD). During the training phase, the top command showed a CPU utilization of 400%, i.e. all four cores where at 100%. The utilization stayed in that range during the sampling phase. The training took 10:20 minutes, the sampling 8 minutes. Thus the total run time was 18 minutes compared to 5 minutes – a factor of 360%. Following the advice on this post, I then played a bit with the settings of the GPU and adjusted the clock rates and the auto boost mode as follows. sudo nvidia-smi --auto-boost-default=0 sudo nvidia-smi -ac 2505,875  That brought the GPU utilization down to a bit less than 50%, but had a comparatively small impact on the run times which now were 1:40 min (instead of 2 min) for training and 2:30 min (instead of 3 min) for sampling. So the total run time was now a bit more than 4 minutes, which is a speed up of roughly 20% compared to the default settings. Compared to the CPU, we have now reached a speed up of almost 4,5. Next, let us compare this to the run time on two CPUs only. To measure that, I grabbed an instance of the t2.large machine type that comes with 2 CPUs – according to /proc/cpuinfo, it is equipped with two Intel Xeon E5-2676 CPUs at 2.40GHz. Interestingly, the training phase only took roughly 8 minutes on that machine, which is even a bit faster than on the p2.xlarge which has four cores. The sampling phase was faster as well, taking only 6 minutes instead of 8 minutes. It seems that adding more CPUs increases the overhead for the synchronisation between the cores drastically so that it results in a performance penalty instead of a performance improvement. To verify this, I did a run on a p2.8xlarge with 32 CPUs and got a similar result – training took 9 minutes, sampling 6:50 minutes. Finally, I could not resist the temptation to try this out on a more advanced GPU enabled machine. So I got a p3.2xlarge instance which contains one of the relatively new Tesla V100 GPUs. I did again adjust the application clocks using sudo nvidia-smi -ac 877,1530  With these settings, one execution now took only about 1:20 minutes for the training and 1:50 min for the sampling. However, the GPU utilization was only at 30% – so we have reached a point where just having a faster GPU does not lead to a significant speed advantage any more. The following table summarizes the results of the various measurements. Instance Run time training Run time sampling p3.2xlarge (Tesla V100) 1:20 min 1:40 min p2.large (Tesla K80) 1:40 min 2:30 min p2.large (4 x CPU) 10 min 8 min p2.8xlarge (32 x CPU) 9 min 6:50 min t2.large (2 x CPU) 8 min 6 min Of course we could now start to optimize the implementation. For the training phase, I assume that the bottleneck that limits GPU utilization is the use of the feed dictionary mechanism which could be replaced by queues to avoid overhead of switching back between CPU and GPU. During the sampling phase, we could also try to reduce the relative overhead of the run method by combining a certain number of steps – say 10 – into the graph and thus reducing the number of iterations that happen outside of the model. It would be interesting to play with this and see whether we can improve the performance significantly. But this is already a long post, so I will leave this for later… ### References 1. R. Raina, A. Madhavan, A. Ng, Large-scale Deep Unsupervised Learning using Graphics Processors, Proceedings of the 26 th International Conference on Machine Learning (2009) 2. K. Chellapilla, S. Puri , P. Simard, High Performance Convolutional Neural Networks for Document Processing, International Workshop on Frontiers in Handwriting Recognition (2006) ## Training restricted Boltzmann machines with persistent contrastive divergence In the last post, we have looked at the contrastive divergence algorithm to train a restricted Boltzmann machine. Even though this algorithm continues to be very popular, it is by far not the only available algorithm. In this post, we will look at a different algorithm known as persistent contrastive divergence and apply it to the BAS data set and eventually to the MNIST data set. Recall that one of the ideas of contrastive divergence is to use a pattern from the sample set as a starting point for a Gibbs sampler to calculate the contribution of the negative phase to the weight update. The idea behind persistent contrastive divergence (PCD), proposed first in [1], is slightly different. Instead of running a (very) short Gibbs sampler once for every iteration, the algorithm uses the final state of the previous Gibbs sampler as the initial start for the next iteration. Thus, in every iteration, we take the result from the previous iteration, run one Gibbs sampling step and save the result as starting point for the next iteration. This amounts to running one long chain of states that are related by Gibbs sampling steps. Of course this is not exactly one longs Gibbs sampler, as the weights and therefore the probability distribution changes with each step. However, the idea is that when the learning rate is small, the weight change during two subsequent iterations is neglegible, and we effectively create one long Gibbs sampler which provides a good approximation to the actual distribution. In practice, one often uses several chains that are run in parallel. Such a chain is sometimes called a negative particle. It is recommended in [1] to chose the number of particles to be equal to the batch size. In an implementation in Python, we can store the state of the negative particles in a matrix N where each row corresponds to one particle. The idea to form one long Markov chain obviously works best if the learning rate is very small. On the other hand, this slows down the convergence of the gradient descent algorithm. In order to solve this, it is common to reduce the learning rate over time, for instance linearly with the number of iterations. A second additional improvement that is usually implemented is a weight decay. Essentially, a weight decay is an additional penalty that is applied to avoid that the weights grow too large which would slow down the sampling procedure. Let us now see how the PCD algorithm can be coded in Python. We will again store the model parameters and the state in a Python class. In the __init__ method of that class, we initialize the weights and the bias vectors and also set the particles to some randomly chosen initial value. class PCDRBM (Base.BaseRBM): def __init__(self, visible = 8, hidden = 3, particles = 10, beta=2.0): self.visible= visible self.hidden = hidden self.beta = beta self.particles = particles # # Initialize weights with a random normal distribution # self.W = np.random.normal(loc=0.0, scale=0.01, size=(visible, hidden)) # # set bias to zero # self.b = np.zeros(dtype=float, shape=(1, visible)) self.c = np.zeros(dtype=float, shape=(1, hidden)) # # Initialize the particles # self.N = np.random.randint(low=0, high=2, size=(particles,self.visible)) self.global_step = 0  Assuming that we have a method runGibbsStep that runs one Gibbs sampling step with the given weight starting at some initial state, one iteration of the PCD algorithm now looks as follows. # # Update step size - we do this linearly over time # step = initial_step_size * (1.0 -(1.0*self.global_step)/(1.0*iterations*epochs)) # # First we compute the negative phase. We run the # Gibbs sampler for one step, starting at the previous state # of the particles self.N # self.N, _ = self.runGibbsStep(self.N, size=self.particles) # # and use this to calculate the negative phase # Eb = expit(self.beta*(np.matmul(self.N, self.W) + self.c)) neg = np.tensordot(self.N, Eb, axes=((0),(0))) # # Now we compute the positive phase. We need the # expectation values of the hidden units # E = expit(self.beta*(np.matmul(V, self.W) + self.c)) pos = np.tensordot(V, E, axes=((0),(0))) # # Now update weights # dW = step*self.beta*(pos -neg) / float(batch_size) - step*weight_decay*self.W / float(batch_size) self.W += dW self.b += step*self.beta*np.sum(V - self.N, 0) / float(batch_size) self.c += step*self.beta*np.sum(E - Eb, 0) / float(batch_size) self.global_step +=1  As always, the full source code is available from my machine learning GitHub repository. I have enhanced the code in RBM.py so that it accepts a command line parameter --algorithm that lets you choose between ordinary contrastive divergence and the PCD algorithm. Let us now run a few trials. First, we will again use the BAS data set. You can download and run the code from the GitHub repository as follows. $ git clone http://www.github.com/christianb93/MachineLearning.git
$cd MachineLearning$ python RBM.py --algorithm=PCD --run_reconstructions=1 --show_metrics=1


When the script completes, you should again see the two images. The first image displays how the reconstruction errors and weight changes behave during the training.

We see that the reconstruction error (the diagram on the right) decreases slower than it did for the ordinary contrastive divergence algorithm. On the left hand side, where the change of the weights is displayed, we can clearly see the impact of the linearly decreasing step size. The second picture shows again the result of a reconstruction attempt of slightly distorted patterns.

Let us now try out a different application of restricted Boltzmann machines – sampling. After a successful training phase, the model distribution given by the weights should be close to the empirical distribution of the training data. Thus, if we sample from the model distribution, using for instance Gibbs sampling, we should be able to obtain patterns. that somehow resemble the training data.

We will use this to generate handwritten digits based on the well known MNIST data set, more precisely the copy available at mldata.org. To download and read the data set, we use the method fetch_mldata provided by the scikit learn library. We will then train our network for 40.000 epochs using 60 images out of this data set and 128 hidden units and subsequently run 200.000 Gibbs sampling steps starting from a random pattern.

$python RBM.py --algorithm=PCD --data=MNIST --N=28 --epochs=40000 --pattern=60 --hidden=128 --run_samples=1 --sample=200000 --save=1  Note that when you run this for the first time, the MNIST data set will be downloaded and stored in a folder in your home directory, so this might take some time (the file has a bit less than 60 MBytes). The results are already very encouraging. Most patterns resemble a digit closely, only the image at the top left corner did obviously not converge properly. However, we still see a strong bias – only very few of the 9 digits that the data set contains appear. So we probably need to fine tune the parameters like number of hidden units, learning rate, weight decay or the number of epochs to obtain better results. Unfortunately, when you start to play around to optimize this further, you will see that the run time of the algorithm has reached a point where quick iterations to try out different parameters become virtually impossible. I have been running this on my PC that has an Intel Core i7 CPU, and Python was able to distribute this nicely across all four physical cores, taking them to 100% utilization, but still the script was already running for 7 minutes. If we want to increase the number of iterations or the number of hidden units to be able to learn more pattern, the run time can easily go up to almost 30 minutes. Of course professional training of neuronal networks is nowadays no longer been done on a CPU. Instead, modern frameworks use the power of graphical processing units (GPUs) that are optimized for exactly the type of work that we need – highly parallel processing of floating point matrices. Therefore, I will show you in the next post in this series how you can use the TensorFlow framework to move the workload to a GPU. 1. T. Tieleman, Training restricted Boltzmann machines using approximations to the likelihood gradient, International Conference on Machine Learning (ICML), 2008 2. A. Fischer, C. Igel, Training restricted Boltzmann machines: an introduction, Pattern Recognition Vol. 47 (2014), pp 25–39 ## Learning algorithms for restricted Boltzmann machines – contrastive divergence In the previous post on RBMs, we have derived the following gradient descent update rule for the weights. $\Delta W_{ij} = \beta \left[ \langle v_i \sigma(\beta a_j) \rangle_{\mathcal D} - \langle v_i \sigma(\beta a_j) \rangle_{P(v)} \right]$ In this post, we will see how this update rule can be efficiently implemented. The first thing that we note is that the term $\sigma(\beta a_j)$ that appears several times is simply the conditional probability for the hidden unit j to be “on” and, as only the values 0 and 1 are possible, at the same time the conditional expectation value of that unit given the values of the visible units – let us denote this quantity by $e_j$. Our update rule now reads $\Delta W_{ij} = \beta \left[ \langle v_i e_j \rangle_{\mathcal D} - \langle v_i e_j \rangle_{P(v)} \right]$ Theoretically, we know how to calculate this. The first term – the positive phase – is easy, this is just the average over the sample set. The second term is more challenging. Theoretically, we would need a Gibbs sampler to calculate it using a Monte Carlo approach. One step of this sampler would proceed as follows. 1. Given the values v of the visible units, calculate the resulting expectation values e 2. Set hidden unit j to one with probability ej 3. For each visible unit i, calculate the conditional probability pi to be one given the new values of the hidden units 4. Set vi to 1 with probability pi After some burn-in phase, we would then calculate the product $v_i e_j$ after each step and take the average of these values. The crucial point is that for a naive implementation, we would start the Gibbs sampling procedure during each gradient descent iteration from scratch, i.e. with some randomly initialized values for the visible units. One of the ideas behind the algorithm known as contrastive divergence that was proposed by G. Hinton in [1] is to restart the Gibbs sampler not at a random value, but a randomly chosen vector from the data set! The idea behind this is that if we have been running the training for some time, the model distribution should be close to the empirical distribution of the data, so sampling a vector from the data should give us something close to the equilibrium state of the Gibbs sampling Markov chain (if you do not known what a Markov chain is – do not worry and just read on, I will cover Markov chains and the mathematics behind all this in a later post). The second approximation that the contrastive divergence algorithm makes is to replace the expectation values in the positive and negative phase by a point estimate. For the positive phase, that means we simply calculate the value at one point from the data set. For the negative phase, we run the Gibbs sampling procedure – starting as explained above with a vector from the data set – and then simply compute the product $v_i e_j$ for the result. It now turns out that, based on empirical observations, these approximations work extremely well – in fact, it turns out that instead of running a full Gibbs sampler with a few hundred or even a few thousand steps, one step is often sufficient! This is surprising, but open to an intuitive explanation – we run all this within the outer loop provided by the gradient descent algorithm, and if we chose the learning rate sufficiently small, the parameters do not change a lot between these steps, so that we effectively do something that is close to one long Gibbs sampling Markov chain. With these simplifications, the constrastive divergence algorithm now looks as follows. FOR EACH iteration DO Sample a vector v from the data set SET $e = \sigma(\beta( W^T v + c))$ FOR EACH hidden unit DO SET $h_j = 1$ with probability $e_j$ FOR EACH visible unit DO SET $\bar{v}_i = 1$ with probability $\sigma(\beta (W h + b))_i$ SET $\bar{e} = \sigma(\beta (W^T \bar{v} + c))$ SET $W = W + \lambda \beta \left[ v e^T - \bar{v} \bar{e}^T \right]$ SET $b = b + \lambda \beta \left[ v - \bar{v} \right]$ SET $c = c + \lambda \beta \left[ e - \bar{e} \right]$ DONE The first six lines within an iteration constitute one Gibbs sampling step, starting with a value for the visible units from the data set, sampling the hidden units from the visible units and sampling the visible units from the hidden units. In the next line, we recalculate the expectation values of the hidden units given the (updated) values of the visible units. The value $\bar{v}_i \bar{e}_j$ is then the contribution of the negative phase to the update of $W_{ij}$. We can summarize the contributions for all pairs of indices as the matrix $\bar{v} \bar{e}^T$. Similarly, the positive phase contributes with $v e^T$. In the next line, we update W with both contributions, where $\lambda$ is the learning rate. We then apply similar update rules to the bias for visible and hidden units – the derivation of these update rules from the expression for the likelihood function is done similar to the derivation of the update rules for the weights as shown in my last post. Let us now implement this in Python. To have a small data set for our tests, we will use an artificial data set called bars and stripes that I have seen first in [3]. Given a number N, we can create an image with N x N pixels for every number x smallers than 2N as follows. Each row corresponds to one binary digit of x. If this digit is one, the entire row is black, i.e. we have one black vertical stripe, otherwise the entire row is white. A second row of patterns is obtained by coloring the columns similarly instead of the rows. Thus we obtain 2N+1 possible patterns, more than enough for our purposes. I have written a helper class BAS in Python that creates these patterns. Next, let us turn to the actual RBM. We store the current state of the RBM in a class RBM that is initialized as follows. class RBM: def __init__ (self, visible = 8, hidden = 3, beta = 1): self.visible = visible self.hidden = hidden self.beta = beta self.W = np.random.normal(loc = 0, scale = 0.01, size = (visible, hidden)) self.b = np.zeros(shape = (1,visible)) self.c = np.zeros(shape = (1,hidden))  Here W is the weight matrix, beta is the inverse temperature, and b and c are the bias vectors for the visible and hidden units. Next we need a method that runs one step in a Gibbs sampling chain, starting with a state of the visible units captured in a matrix V (we calculate this in a mini-batch for more than one sample at a time, each row in the matrix represents one sample vector). Using once more the numpy library, this can be done as follows. def runGibbsStep(self, V, size = 1): # # Sample hidden units from visible units # E = expit(self.beta*(np.matmul(V, self.W) + self.c)) U = np.random.random_sample(size=(size, self.hidden)) H = (U <= E).astype(int) # # and now sample visible units from hidden units # P = expit(self.beta*(np.matmul(H, np.transpose(self.W)) + self.b)) U = np.random.random_sample(size=(size, self.visible)) return (U <= P).astype(int), E  With this method at hand – which returns the new value for the visible units but the old value for the conditional expectation of the hidden units – we can now code our training routine. def train(self, V, iterations = 100, step = 0.01): batch_size = V.shape[0] # # Do the actual training. First we calculate the expectation # values of the hidden units given the visible units. The result # will be a matrix of shape (batch_size, hidden) # for _ in range(iterations): # # Run one Gibbs sampling step and obtain new values # for visible units and previous expectation values # Vb, E = self.runGibbsStep(V, batch_size) # # Calculate new expectation values # Eb = expit(self.beta*(np.matmul(Vb, self.W) + self.c)) # # Calculate contributions of positive and negative phase # and update weights and bias # pos = np.tensordot(V, E, axes=((0),(0))) neg = np.tensordot(Vb, Eb, axes=((0),(0))) dW = step*self.beta*(pos -neg) / float(batch_size) self.W += dW self.b += step*self.beta*np.sum(V - Vb, 0) / float(batch_size) self.c += step*self.beta*np.sum(E - Eb, 0) / float(batch_size)  Let us now play around with this network a bit and visualize the training results. To do this, clone my repository and then run the simulation using $ git clone  https://github.com/christianb93/MachineLearning.git
$cd MachineLearning$ python RBM.py  --run_reconstructions=1 --show_metrics=1


This will train a restricted Boltzmann machine on 20 images out of the BAS dataset with N=6. For the training, I have used standard parameters (which you can change using the various command line switches, use --help to see which parameters are available). The learning rate was set to 0.05. The number of iterations during training was set to 30.000, and 16 hidden units are used. The inverse temperature $\beta$ is set to 2.0. In each iteration, a mini-batch of 10 patterns is trained.

After every 500 iterations, the script prints out the current value of the reconstruction error. This is defined to be the norm of the difference between the value of the visible units when the Gibbs sampling step starts and the value after completing the Gibbs sampling step, i.e. this quantity measures how well the network is able to reconstruct the value of the visible units from the hidden units alone.

After the training phase is completed, the script will select eight patterns randomly. For each of these patterns, it will flip a few bits and then run 100 Gibbs sampling steps. If the training was successful, we expect that the result will be a reconstruction of the original image, i.e. the network would be able to match the distorted images to the original patterns.

When all the calculations have been completed, the network will display two images. The first image should roughly look like the image below.

This matrix visualizes the result of the reconstruction process described above. Each of the rows shows the outcome for one of the eight selected patterns. The first image in each row is the original pattern from the BAS data set. The second one is the distorted image some pixels have been flipped. The third image shows the result of the reconstruction run after 50 Gibbs iterations, and the last image shows the result after the full 100 iterations.

We see that in most cases, the network is able to correctly reconstruct the original image. However, there are also a fes rows that look suspicious. In the first row, we could hope that the network eventually converges if we execute more sampling steps. In the third row, however, the network converges to a member of the BAS data set, but to the wrong one.

The second diagram that the script produces displays the change to the weights after each iteration and the reconstruction error.

We see that both quantities quickly get smaller, but never stabilize at exactly zero. This is not really surprising – as we work with a non-zero temperature, we will always have some thermal fluctuations and the reconstruction error will never be constantly zero, but oscillate around a small value.

I invite you to play around with the parameters a bit to see how the network behaves. We can change the value of the inverse temperature with the parameter --beta, the number of hidden units with the parameter --hidden, the number of Gibbs steps used during the reconstruction with --sample and the step size with --step. If, for instance, you raise the temperature, the fluctuations of the reconstruction error will increase. If, one the other hand, we choose a very small temperature, the network converges very slowly. Making the step size too small or too large can also lead to non-convergence etc.

That completes this post on contrastive divergence. In the next post, I will show you an alternative algorithm that has gained a lot of popularity called persistent contrastive divergence (PCD), before we finally set out to implement an restricted Boltzmann machine on a GPU using the TensorFlow framework.

1. G. Hinton, Training products of experts by minimizing contrastive divergence, Journal Neural Computation Vol. 14, No. 8 (2002), 1771 1800
2. G. Hinton, A practical guide to training restricted Boltzmann machines, Technical Report University of Montreal TR-2010-003 (2010)
[3] D. MacKay, Information Theory, Inference and learning
algorithms, section 43, available online at this URL

## Restricted Boltzmann machines

In the previous post, we have seen that a Boltzmann machine as studied so far suffers from two deficiencies. First, training is very slow as we have to run a Gibbs sampler until convergence for every iteration of the gradient descent algorithm. Second, we can only see the second moments of the data distribution and the learning rule ignores higher moments.

A class of networks called Restricted Boltzmann machines (RBM) has been designed to overcome these problems. An RBM is a Boltzmann machine with two additional architectural features. First, it has hidden units. This simply means that we split the set of all units in the network into two disjoint sets called visible units and the said hidden units. When we we train the network, we connect the data samples only to the visible units. The hidden units, however, also follow the dynamical rules of the network and serve as latent variables – you can think of them as additional parameters of the network which are adapted during training but are not directly prescribed by the training set, similar to a hidden layer in a feed-forward neuronal network.

Second, in a restricted Boltzmann machine, certain restrictions on the weights are in effect. Specifically, we only allow hidden units to be connected to visible units and vice versa, so there are no connections between hidden units and no connections between visible units. Effectively, a restricted Boltzmann machine is therefore organised in two layers – one layer containing the hidden units and one layer containing the visible units, as shown below.

What does this imply for the mathematical description of the network? In fact, we will see that this simplifies things considerably. First, corresponding to the differentiation between hidden and visible units, our index set can be written as

$\{ 1, \dots, N \} = I_v \cup I_h$

so that unit i is a hidden unit if i is in the set $I_v$ and a hidden unit if i is in the set $I_h$. Second, it is common to use 0 and 1 as states instead of -1 and +1. Our state space then splits

$\{ 0, 1\}^N = {\mathcal S} = {\mathcal V} \times \mathcal {H}$

and correspondingly we can write any state as

$s = (v,h)$

where v specifies the state of the visible units and h the state of the hidden units. As only visible units correspond to actual input, the purpose of the training phase is now to adjust the marginal distribution

$P(v) = \sum_h P(v,h) = \frac{1}{Z} \sum_h e^{-\beta E(v,h)}$

such that is it as close as possible to the empirical distribution of the test data.

The expression for the energy also simplifies greatly, as all terms involving only hidden units and only visible units disappear. If we replace the matrix W that contains all connections by a reduced matrix – that we again call W – that only contains the remaining connections between visible and hidden units, we can express the energy as

$E(v,h) = - \sum_{i \in I_v, j \in I_h} W_{ij} v_i h_j$

In addition, we will now also add an explicit bias to both the hidden and visible units, so that our full energy is

$E(v,h) = - \sum_{i \in I_v, j \in I_h} W_{ij} v_i h_j - \sum_i v_i b_i - \sum_j h_j c_j$

Of course the matrix W is now no longer symmetric and not even quadratic (as the number of hidden units will in general not be the same as the number of visible units).

We can now again calculate the update rules as before. First, we write down the likelihood function

$l({\mathcal D} | W) = - \frac{1}{K} \ln P({\mathcal D} | W) = - \frac{1}{K} \sum_k \ln \sum_h e^{-\beta E(v^{(k)},h)}+ \ln Z$

where now $v^{(k)}$ is the k-the sample point corresponding to a set of values for the visible units.

Again we will need the derivatives of this with respect to the weights. For the second term – the logarithm of the partition function – we have already seen in the last post how this works. Recalling the results from this post, we easily find that

$\frac{\partial}{\partial W_{ij}} \ln Z = - \beta \langle \frac{\partial E}{\partial W_{ij}} \rangle_P = \beta \langle v_i h_j \rangle_P$

so that the derivative is again an expectation value which we could try to approximate using a sample of the model distribution. The first term requires a bit more work. Let us first calculate

$\frac{\partial }{\partial W_{ij}} \ln \sum_h e^{-\beta E(v,h)} = \frac{1}{Z P(v)} \sum_h \frac{\partial }{\partial W_{ij}} e^{-\beta E(v.h)}= - \beta \sum_h \frac{\partial E(v,h)}{\partial W_{ij}} P(h | v)$

But this is again an expectation value, this time it is an expectation value with respect to the conditional distribution of the hidden units given the visible units.

$\frac{\partial }{\partial W_{ij}} \ln \sum_h e^{-\beta E(v,h)} = - \beta \langle \frac{\partial E(v,h)}{\partial W_{ij}} \rangle_{P(\cdot | v)}$

The derivative of the energy with respect to the weights is as above, and we finally obtain the following update rule for the weights:

$\Delta W_{ij} = \lambda \beta \left[ \langle \langle v_i h_j \rangle_{P(\cdot | v)} \rangle_{\mathcal D} - \langle v_i h_j \rangle_P \right]$

Note that the first term is a double expectation value – for each sample $v^{(k)}$ for the visible units, we use the expectation value under the conditional distribution over the hidden units given this value for the visible units.

Now let us start to simplify this expression a bit further, leveraging the restrictions on the geometry of the network. Let us first try to find an expression for the conditional probability

$P(h_j = 1 | v)$

This is in fact easy to calculate in our situation. As the state of a hidden unit does not depend on the other hidden units, but only on the visible units, we find that

$P(h_j = 1 | v)= \sigma(\beta (\sum_i W_{ij} v_i + c_j)) = \sigma(\beta a_j)$

where

$a_j = \sum_i W_{ij} v_i + c_j$

is the activation of the hidden unit j. Using this, we can already simplify the first term in the update rule as follows:

$\langle v_i h_j \rangle_{P(\cdot | v)} = \sum_h P(h | v) v_i h_j = v_i \sum_{h : h_j = 1} P(h | v)$

But this is of course nothing but

$v_i P(h_j = 1 | v)$

so that we eventually find

$\langle v_i h_j \rangle_{P(\cdot | v)} = v_i \sigma (\beta a_j)$

A similar argument works for the second term in the update rule. We have

$\langle v_i h_j \rangle_P = \sum_v \sum_h v_i h_j P(v,h) = \sum_v v_i P(v) \sum_h h_j P(h | v)$

Now the second term sum is again the conditional probability for $h_j$ to be one given v, so that this turns into

$\langle v_i h_j \rangle_P = \sum_v v_i P(v) \sigma(\beta a_j) = \langle v_i \sigma(\beta a_j) \rangle_{P(v)}$

We therefore finally obtain the following simplified update rule.

$\Delta W_{ij} = \beta \left[ \langle v_i \sigma(\beta a_j) \rangle_{\mathcal D} - \langle v_i \sigma(\beta a_j) \rangle_{P(v)} \right]$

Thus again, we see that the gradient is composed of two terms, which we call the positive phase and the negative phase. In each phase, we sample the same expression, once over the data distribution and once over the marginal distribution.

How do we actually calculate these terms? The positive phase is easy – we have written this as an expectation value, but it is nothing but an ordinary sum. For each vector in the sample, we calculate the activation of the hidden unit j, apply the multiplication by $\beta$ and the sigmoid function and multiply the result with the value of the visible unit. So this is in fact an easily calculated analytical expression.

Whereas we have found an analytic expression for the positive phase, there is no obvious analytic expression for the negative phase, so we again need a sampling procedure to calculate this term. At this point, the special structure of the network again helps to make the sampling easier. Suppose we wanted to apply an ordinary Gibbs sampler, where instead of choosing the neuron that we update next randomly, we cycle sequentially through all the neurons. We could then do all the hidden neurons first and then continue with the visible units. Now, as the visible units only depend on the hidden units and vice versa, we could as well update all hidden units in parallel and then all visible units in parallel, using that as in the case of hidden units, the conditional probability for a visible unit to be one can be expressed as

$P(v_i = 1 | h) = \sigma(\beta (\sum_j W_{ij} h_j + b_i))$

This procedure is called Gibbs sampling with block updates. It is also obvious that sampling from the joint distribution $P(v,h)$ in this way and then ignoring the values of the hidden units in this way gives a sampler for the marginal distribution.

Therefore our algorithm to calculate the second term of the update rule would be as follows. We would start with some value for the visible units. Then we would calculate the probability that each hidden unit is on given these values for the visible units and update the hidden units according to this distribution. We would then use the new values for the hidden units, calculate the conditional distribution of the visible units and update the visible units according to this distribution. This would constitute a full Gibbs sampling step. We would repeat this process until convergence is reached and then sample for a few steps to calculate the expectation values above. Plugging this into the update rule and calculating the first term analytically, we would then obtain the needed update for the weights.

So it looks like we are back to our old problem – to calculate one weight update during the gradient descent procedure, we have to run a Gibbs sampler to convergence. Fortunately, it turns out that several approximations exist that make this calculation feasible. Next, we will look at two of these approaches – constrastive divergence and its companion persistent contrastive divergence (PCD). We will then implement both algorithms in Python and try it out, first on a small sample set and then finally on the MNIST data set. But this post has already grown a bit lengthy – so let us save this for the next post in this series.

## Hopfield networks: practice

After having discussed Hopfield networks from a more theoretical point of view, let us now see how we can implement a Hopfield network in Python.

First let us take a look at the data structures. We will store the weights and the state of the units in a class HopfieldNetwork. The weights are stored in a matrix, the states in an array.

class HopfieldNetwork:

#
# Initialize a Hopfield network with N
# neurons
#
def __init__(self, N):
self.N = N
self.W = np.zeros((N,N))
self.s = np.zeros((N,1))


Next we write a method for the update rule. In a matrix notation, the activation of unit i is given as the dot product of the current state and the i-th row of the weight matrix (or the i-th column, as the matrix is symmetric). Therefore we can use the following update function.

#
# Run one simulation step
#
def runStep(self):
i = np.random.randint(0,self.N)
a = np.matmul(self.W[i,:], self.s)
if a < 0:
self.s[i] = -1
else:
self.s[i] = 1


Finally, there is the Hebbian learning rule. If we store the sample set in a matrix S such that each row corresponds to one sample, the learning rule is equivalent to

$W = S^T S$

Thus we can again use the matrix multiplication from the numpy package, and our learning rule is simply

def train(self, S):
self.W = np.matmul(S.transpose(), S)


Now we need some pattern to train our network. For the sake of demonstration, let us use a small network with 5 x 5 units, each of them representing a pixel in a grayscale 5 x 5 image. For this post, I have hardcoded five simple patterns, but we could of course use any other training set.

To illustrate how the Hopfield network operates, we can now use the method train to train the network on a few of these patterns that we call memories. We then take these memories and randomly flip a few bits in each of them, in other words we simulate random errors in the pattern. We then place the network in these states and run the update rule several times. If you want to try this yourself, get the script Hopfield.py from my GitHub repository.

The image above shows the result of this exercise. Here we have stored three memories – the first column of images – in the network. The second image in each row then shows the distorted versions of these patterns after flipping five bits randomly. The next images in each row show the state of the network after 20, 40, 60, 80 and 100 iterations of the update rule. We see that in this case, all errors could be removed and the network did in fact converge to the original image.

However, the situation becomes worse if we try to store more memories. The next image shows the outcome of a simulation with the same basic parameters, but five instead of three memories.

We clearly see that not a single one of the distorted patterns converges to the original image. Apparently, we have exceeded the capacity of the network. In his paper, Hopfield – based on theoretical considerations and simulations – argues that the network can only store approximately $0.15 \cdot N$ patterns, where N is the number of units. In our case, with 25 units, this would be approximately 3 to 4 patterns.

If we exceed this limit, we seem to create local minima of the energy that do not correspond to any of the stored patterns. This problem is known as the problem of spurious minima which can also occur if we stay below the maximum capacity – if you do several runs with three memories, you will find that also in this case, spurious minima can occur.

The update rule of the Hopfield network is deterministic, its energy can never increase. Thus if the system moves into one of those local minima, it can never escape again and gets stuck. An Ising model at a finite, non-zero temperature behaves differently. As the update rule is stochastic, it is possible that the system moves away from a local minimum in a Gibbs sampling step, and therefore has the chance to escape from a spurious minimum. This is one of the reasons why researchers have tried to come up with stochastic versions of the Hopfield network. One of these stochastic versions is the Boltzmann network, and we will start to look at its theoretical foundations in the next post in this series.

## Hopfield networks: theory

Having looked in some detail at the Ising model, we are now well equipped to tackle a class of neuronal networks that has been studied by several authors in the sixties, seventies and early eighties of the last century, but has become popular by an article [1] published by J. Hopfield in 1982.

The idea behind this and earlier research is as follows. Motivated by the analogy between a unit in a neuronal network and a neuron in a human brain, researchers were trying to understand how the neurons needed to be organized to be able to create abilities like associative memories, i.e. a memory that can be navigated by associations that bring up additional stored memories. To explain how the human brain organizes the connections between the neurons in optimal (well, as least useful) way, analogies with physical systems like the Ising model covered in this post which also exhibit some sort of spontaneous self organization, were pursued.

In particular, the analogy with stability attracted attention. In many physical systems, there are stable states. If the system is put into a state which is sufficiently close to such a stable state, it will, over time, move back into that stable state. A similar property is desirable for associative memory systems. If, for instance, such a system has memorized an image and is then placed in a state which is somehow close to that image, i.e. only a part of the image or a noisy version of the image is presented, it should converge into the memorized state representing the original image.

With that motivation, Hopfield described the following model of a neuronal network. Our network consists of individual units that can be in any of two states, “firing” and “not firing”. The system consists of N such units, we will denote the state of unit i by $s_i$.

Any two units can be connected, and there is a matrix W whose elements represent the strength of the connection between the individual units, i.e. $w_{ij}$ is the strength of the connection between the units i and j. We assume that no neuron is connected to ifself, i.e. that $w_{ii} = 0$, and that the matrix of weights is symmetric, i.e. that $w_{ij} = w_{ji}$.

The activation of unit i is then obtained by summing up the weighted values of all neurons connected to it, i.e. given by

$a_i = \sum_j w_{ij} s_j$

Hopfield used a slightly different notation in his paper and assigned the values 0 and 1 to the two states, but we will again use -1 and +1.

So how does the Hopfield network operate? Suppose that the network is in a certain state. i.e. some of the neurons will be “firing”, represented by the value +1, and others will be passive, represented by the value -1. We now choose a neuron at random and calculate its activation function according to the formula above. We then determine the new state by the rule

$s_i' = \begin{cases} +1 & a_i \geq 0 \\ -1 & a_i < 0 \end{cases}$

In most cases, the network will actually converge after a finite number of steps, i.e. this rule does not change the state any more. To see why this happens, let us consider the function

$E(s) = - \frac{1}{2} \sum_{i,j} w_{ij} s_i s_j$

which is called the energy function of the model. Suppose that we pass from a state s to a state s’ by applying the update rule above. Let us assume that we have updated neuron i and changed its state from $s_i$ to $s_i'$. Let

$\Delta s_i = s_i' - s_i$

Using the fact that the matrix W is symmetric, we can then write

$E(s') = -\frac{1}{2} \sum_{p,q \neq i} s_p s_q - \sum_p w_{pi} s_i' s_p$

which is the same as

$-\frac{1}{2} \sum_{p,q \neq i} s_p s_q - \sum_p w_{pi} s_i s_p - \sum_p w_{pi} (s_i' - s_i) s_p$

Thus we find that

$E(s') = E(s) - \Delta s_i \sum_p w_{ip} s_p$

Now the sum is simply the activation of neuron i. As our update rule guarantees that the product of $\Delta s_i$ and the activation of unit i is never negative, this implies that during the upgrade process, the energy function will always increase or stay the same. Thus the state will settle in a local minimum of the energy function.

At this point, we can already see some interesting analogies with the Ising model. Clearly, the units in a Hopfield network correspond to the particles in an Ising model. The state (firing or not) corresponds to the spin (upward or downward). The energy is almost literally the same as the energy of the Ising model without an external magnetic field.

Also the update rules are related. Recall that during a Gibbs sampling step for an Ising model, we calculate the conditional probability

$P = \sigma(2 \beta \langle J_i, s \rangle)$

Here the scalar product is the equivalent of the activation, and we could rewrite this as

$P = \sigma(2 \beta a_i)$

Let us now assume that the temperature is very small, so that $\beta$ is close to infinity. If the activation of unit i is positive, the probability will be very close to one. The Gibbs sampling rule will then almost certainly set the spin to +1. If the activation is negative, the probability will be zero, and we will set the spin to -1. Thus the update role of a Hopfield network corresponds to the Gibbs sampling step for an Ising model at temperature zero.

At nonzero temperatures, a Hopfield network and an Ising model start to behave differently. The Boltzmann distribution guarantees that the state with the lowest energies are most likely, but as the sampling process proceeds, the random element built into the Gibbs sampling rule implies that a state can evolve into another of higher energy as well, even though this is unlikely. For the Hopfield network, the update rule is completely deterministic, and the states will always evolve into states of lower or at least equal energy.

The memories that we are looking for are now the states of minimum energy. If we place the system in a nearby state and let it evolve according to the update rules, it will move over time back into a minimum and thus “remember” the original state.

This is nice, but how do we train a Hopfield network? Given some state s, we want to construct a weight matrix such that s is a local minimum. More generally, if we have already defined weights giving some local minima, we want to adjust the weights in order to create an additional minimum at s, if possible without changing the already existing minima significantly.

In Hopfields paper, this is done with the following learning rule.

$w_{ij} = \begin{cases} \sum_s S^{(s)}_i S^{(s)}_j & i \neq j \\ 0 & i = j \end{cases}$

where $S^{(1)}, \dots, S^{(K)}$ are the states that the network should remember (in a later post in this series, we will see that this rule can be obtained as the low temperature limit of a training algorithm called contrastive divergence that is used to train a certain class of Boltzmann machines).

Thus a state S contributes with a positive value to $w_{ij}$ if $S_i$ and $S_j$ have the same sign, i.e. are in the same state. This corresponds to a rule known as Hebbian learning rule that has been postulated as a principle of learning by D. Hebb and basically states that during learning, connections between neurons are enforced if these neurons fire together ([2], chapter 4).

Let us summarize what we have done so far. We have described a Hopfield network as a fully connected binary neuronal network with symmetric weight matrices and have defined the update rule and the learning rule for these networks. We have seen that the dynamics of the network resembles that of an Ising model at low temperatures. We now expect that a randomly chosen initial state will converge to one of the memorized states and that therefore, this model can serve as an associative memory.

In the next post, we will put this to work and implement and train a Hopfield network in Python to study its actual behavior.

### References

1. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proc. Nat. Acad. Sci. Vol. 79, No. 8 (1982), pp. 2554-2558
2. D.O. Hebb, The organization of behaviour, Wiley, New York 1949

## Boltzmann machines, spin, Markov chains and all that

The image above displays a set of handwritten digits on the left. They look a bit like being sketched on paper by someone in a hurry and then scanned and digitalized, not very accurate but still mostly readable – but they are artificial, produced by a neuronal network, more precisely a so called restricted Boltzmann machine.

On the right hand side, you see the (core part of) the code that has been used to produce this image. These are about forty lines of code, and there is some code around it which is not shown, but stripping off all the comments and boilerplate code, we could probably fit the algorithm into less than fifty lines of code.

I found this contrast always fascinating. Creating something that resembles handwritten digits sounds incredibly complex, but can be done with an algorithm that can  be quenched into a comparatively short program – how does that work? So I started to dig deeper, trying to understand neuronal networks in general and in particular Boltzmann machines – the mathematical foundations, the algorithm and the implementation.

Boltzmann machines are a rather special class of neuronal networks that have a reputation of being hard to train, but are important from a theoretical point of view, being closely related to seemingly remote fields like thermodynamics, statistical mechanics and stochastical processes. That makes them interesting, but also hard to understand. I embarked on that journey a couple of months ago, and I thought it would be nice to create a series of blog posts on this. My current thinking is to have one or two posts on each of the following topics over the next couple of weeks.

This is a lot of content, and please do not expect to see one post every other day. But maybe I should just start and we will see how it goes….

So let me try to roughly sketch what a Boltzmann machine is. First, it is a neuronal network. As such it is composed of units that can be compared to the neurons in the nervous system. Similar to a neuron, each unit has an input and an output.  The output of a neuron can serve as input for another neuron. In general, every neuron receives inputs from many other neurons and delivers outputs to many other neurons.

The diagram above shows a very simple neuronal network. It consists of four neurons. The three of them on the left serve as input to the network. Think of them as the equivalent of cells in – say – your visual cortex that are activated by some external stimulus. The cell on the right is the output of the network. Its activation is computed based on the outputs of the neurons on the left and some parameters of the network called the weights which model the strength of the connection between the neurons and which we denote by $w_i, i = 0, 1, 2$, as follows.

$o = \sigma(w_0 x_0 + w_1 x_1 + w_2 x_2) = \sigma(w^T X)$

Here $\sigma$ is a function called the activation function and $X$ is the vector that is formed by $x_0, x_1$ and $x_2$. There are a few standard choices for the activation function, a common one being the sigmoid function.

How is such a network applied? Let us look at this for a problem which is the “Hello World” of machine learning – recognition of handwritten digits. In that problem, you start with a collection of digitized images of handwritten digits, like those that are known as the MNIST database. These images have 28×28 = 784 pixels. We want to design a neuronal network that can classify these images. Such a network should consequently have 784 input units and 10 output units (bear with me that I did not produce a picture of that network, even though it would probably be fun to do this with Neo4J). We present an image to the network by setting the value of the input unit i to the intensity of pixel i. Our aim is to adjust the weights in such a way that if the image represents digit n, only output n is significantly different from zero. This will allow us to classify unknown images – we simply present the image to the network and then see which output is activated.

But how do we find the correct values for the weights? We need weights that connect each of the ten output units to each of the 784 input units, i.e. we have 7840 weights. Thus we are looking for a point in a 7840 dimensional vector space – not easy. Here the process of training comes into play. We take our set of sample images and present them to the network, with initially randomly chosen weights. The output will then not be what we want, but differ from the target output by an error. That error can be expressed as a function of the weights.  The task is then to find a minimum of the error function, and there are ways to do this, most notably the procedure which is known as gradient descent.

As the name suggests, we need the gradient of the error function for that purpose. Fortunately, for the type of neuronal network that we have sketched so far, the gradient can be computed fairly easily – in fact the activation function is chosen on purpose to make dealing with the derivatives easy. We end up with a comparatively simple training algorithm for this type of network and maybe I will show how a simple implementation in Python in a later post – but for now let us move on to Boltzmann machines.

Boltzmann machines or more precisely restricted Boltzmann machines (RBMs) are also composed of units and weights, but work a bit differently.  There are inputs, which are usually called visible units. But there are no classical outputs. Instead, there is a layer of units that is connected to the visible units and is called hidden units, as shown in the following picture.

In the example of handwritten digits again, you would again have 784 visible units. However, the hidden units would not obviously correlate with the digit represented by the input. Instead, you would have a more or less arbitrary number of hidden units, say 300.

During training, you present the network one image, again by setting the values of the visible units (the input) to the pixel intensities. You then compute the value of the hidden units – but you do not do this deterministically, but bring in some random element. Roughly speaking, if the combined input to a hidden unit is p, you set the value of the unit to one with probability p. Then this process is repeated, this time starting from the hidden units. This gives you certain values for the visible units. You then compare this value to the original value and try to adjust the weights such that you get as closely as possible to the original input (this is not exactly true and a not very precise description of one of the possible learning algorithms called contrastive divergence, but we will get more precise later on).

If you succeed, you will be able to reconstruct the value of the visible units from the values of the hidden units. But there are less hidden units, so that the network has apparently learned a condensed representation of the input that still captures the essence of the input. In the case of digits, you can visualize the state of the network after training and obtain something like

These are visualizations of the weights connected to some of the hidden units of a Bolzmann machine after the training phase. We can see that some of the units have appearantly “learned” some characteristic features of the digits, like the vertical stroke that appears in the digits one and seven. These features can be used for several purposes.

First, we can use the features as input for other neuronal networks. We now have only 300 inputs instead of originally 784, and that might simplify the problem a bit and make the process of training the next network easier.

Or, we could start with some random values for the hidden units and calculate the resulting values of the visible units to create sample images – and in fact this is basically what I have done to produce the samples at the top of this post (using an algorithm called PCD, but we will get to this).

Note that Boltzmann machines differ significantly from the type of neural network that we have considered earlier. One major difference is that to train a Boltzmann machine, you do not need to know the digit that the image represents. At no point have we used the information that the images in our database represent ten different digits – that information was not used in the design of the network nor during the training. This approach to machine learning is called unsupervised learning and is obviously very versatile – you do not have to tell the machine what the structure of the data is, it will detect the features independently.

Second, a Boltzmann machine can not only classify images, but can also create images that resemble a given set of input data. That can be used to reconstruct partially available images or to create new images from scratch – networks with this ability are called generative networks.

The price we have to pay for this is that Boltzmann machines are hard to train. The point is that we can still define some sort of error function, but we cannot easily calculate its gradient – there is no straightforward analytic expression for it that could be evaluated within a reasonable amount of time (if you know statistical physics a bit, that might remind you of the partition function, and that is more than pure coincidence, as I will show you in a later post). So we need to approximate the gradient. Technically, the gradient is an integral

$\int f(x) dx$

for some known, but complicated function $f$. Even if we cannot find an analytic expression for this, we can try to approximate it. Your first idea might be “Riemann sums”, but it turns out that this is not a good idea, as our function lives in the space of all weights which has a very high dimension. Instead, we will use an approach called Monte Carlo integration where we represent the integral as an expectation value, draw a sample and approximate the expectation value by the sample average. This is where stochastical methods like Markov chains will come into play.  And finally we will see that the behaviour of our network during training has some striking analogies with the behaviour of certain physical systems like solids exposed to a magnetic field at low temperatures, which are described by a model called the Ising model, and learn how techniques that physicists have  developed for this type of problems apply to neuronal networks.

That is it for today – I hope I could give you a rough idea of what is ahead of us. At least I hope that you start to be curious how all this works out – so looking forward to the next post where I will start with some background from physics.