Building a Network

To go through the quickstart interactively, just fire up Python and we will make everything in the interpreter:

$ python
Python 2.5.2 (r252:60911, Sep 17 2008, 11:21:23)
[GCC 4.0.1 (Apple Inc. build 5465)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>

In PyBrain, networks are composed of Modules which are connected with Connections. You can think of a network as a directed acyclic graph, where the nodes are Modules and the edges are Connections. This makes PyBrain very flexible but it is also not necessary in all cases.

The buildNetwork Shortcut

Thus, there is a simple way to create networks, which is the buildNetwork shortcut:

>>> from pybrain.tools.shortcuts import buildNetwork
>>> net = buildNetwork(2, 3, 1)

This call returns a network that has two inputs, three hidden and a single output neuron. In PyBrain, these layers are Module objects and they are already connected with FullConnection objects.

Activating a Network

The net is already initialized with random values - we can already calculate its output:

>>> net.activate([2, 1])
array([-0.98646726])

For this we use the .activate() method, which expects a list, tuple or an array as input.

Examining the structure

How can we examine the structure of our network somewhat closer? In PyBrain, every part of a network has a name by which you can access it. When building networks with the buildNetwork shortcut, the parts are named automatically:

>>> net['in']
<LinearLayer 'in'>
>>> net['hidden0']
<SigmoidLayer 'hidden0'>
>>> net['out']
<LinearLayer 'out'>

The hidden layers have numbers at the end in order to distinguish between those.

More sophisticated Networks

Of course, we want more flexibility when building up networks. For instance, the hidden layer is constructed with the sigmoid squashing function per default: but in a lot of cases, this is not what we want. We can also supply different types of layers:

>>> from pybrain.structure import TanhLayer
>>> net = buildNetwork(2, 3, 1, hiddenclass=TanhLayer)
>>> net['hidden0']
<TanhLayer 'hidden0'>

There is more we can do. For example, we can also set a different class for the output layer:

>>> from pybrain.structure import SoftmaxLayer
>>> net = buildNetwork(2, 3, 2, hiddenclass=TanhLayer, outclass=SoftmaxLayer)
>>> net.activate((2, 3))
array([ 0.6656323,  0.3343677])

We can also tell the network to use a bias:

>>> net = buildNetwork(2, 3, 1, bias=True)
>>> net['bias']
<BiasUnit 'bias'>

This approach has of course some restrictions: for example, we can only construct a feedforward topology. But it is possible to create very sophisticated architectures with PyBrain, and it is also one of the library’s strength to do so.