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\Huge\bf
Homework \#\,\,2\\
\Large\bf
15-496/782: Artificial Neural Networks\\
Dave Touretzky\\[.2in]
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\begin{itemize}

\item Due February 4, 2004. Answers must be typed.

\item Software you need is in \verb+/afs/cs/academic/class/15782-s04/matlab/bp+


\end{itemize}

\section*{Problems}

\begin{enumerate}

\item Consider a multilayer perceptron whose hidden units use
$x^5$ and whose output units use $\cos(2x)$ as the transfer function,
rather than the usual sigmoid or $\tanh$.  Using the chain rule,
starting from $\partial E/\partial y_k$, derive the formulas for the
weight updates $\Delta w_{jk}$ and $\Delta w_{ij}$.  Your final
formulas should be purely algebraic, i.e., they should not contain
partial derivatives.

\item The XOR function is the 2-bit parity problem.  Consider the 4-bit
parity problem: the input is a four element binary vector, and the
output is a 0 or a 1 depending on whether the input contains an even
or an odd number of ones.  There are 16 possible input patterns.
Parity is a hard problem because changing any one bit of the input
causes the desired output to flip.

\begin{enumerate}

\item
Write code to train a backprop network to solve this problem.  Your
code should use the \verb+bp_innerloop+ routine.  Look at demos like
{}\verb+bpxor+ and \verb+encoder+ to see how to set up the input and
output patterns, and the training loop.  To make learning a little
easier, use targets of $+0.8$ and $-0.8$ instead of 0 and 1.  Use a
learning rate of 0.04, and no momentum.

\item 
Plot the set of 16 output values, \verb+Result2+, as a line with 16
points numbered 0--15.  Update this plot every 10 epochs.  Also plot
the desired outputs as a set of points (not a line); use a different
symbol and a different color.  This will help you monitor the
network's progress as it tries to get all the patterns to come out
right.  Note: in order to superimpose two plots, you will need to use
the command
\verb+hold on+.

\item Also plot the output of one of the hidden units as a set of 
points superimposed on your other plots.  Try to analyze what that
hidden unit is doing, e.g., ``this unit is for looking input patterns
where bit 2 is on and bit 4 is off; it ignores the other two bits.''

\item Be sure to answer every part of this question.
(i) How many epochs does it take to learn this problem using 5 hidden
units?  Run your network ten times and record the number of epochs
each time, and the mean and standard deviation for the ten runs.  (Use
Matlab's \verb+mean+ and \verb+std+ functions.)  (ii) Now run the same
experiment using only 4 hidden units.  (iii) Now try training with 20
hidden units. (iv) How about 100 hidden units?  (v) Can the network
ever solve the problem with only three hidden units?  Use a learning
rate of 0.015 for this experiment. (vi) Two hidden units are not
enough to solve four-bit parity.  But how well can the network do,
i.e., how many of the 16 inputs cases can it get right, where
``right'' just means the output has the correct sign?

\end{enumerate}
\end{enumerate}

\noindent
Turn in a source listing, a sample plot, a table listing your
observations about training time as a function of number of hidden
units, and your analysis of one hidden unit.

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