How to create a multi-layer perceptron in Matlab for a multi-class dataset
<p>I want to create a double layered perceptron for an assignment.<br>
It will act as a classifier for the Fisher iris data set.</p>

<p>I've come across the following problems while creating the network:</p>

<ul>
<li>For a dataset like the above, can i setup the network with a single
input and pass the whole training matrix of n rows and 4 features as
an input value? Or, do i need to adjust the network to use 4 input
node, 1 for each feature, and pass the training matrix as an input
value?</li>
<li>Furthermore, how do I properly connect the layers with each other?<br>
For instance, when i create a perceptron with 4 inputs using the
network command, I don't really understand what do the biasConnect, inputConnect and layerConnect actually do.</li>
<li>Lastly, how many outputs do i need to correctly classify one element?
Every example I've come across uses one output, but, will just one
suffice? Or, do I need one for each class?</li>
</ul>

<p>Command used for creating the network mentioned at the second point:</p>

<pre><code>net = network(4, 2, [1; 0], [1 1 1 1;0 0 0 0], [0 0; 1 0], [0 1]);
</code></pre>