How to train a neural network to supervised data set using pybrain black-box optimization?

Question

I have played around a bit with pybrain and understand how to generate neural networks with custom architectures and train them to supervised data sets using the backpropagation algorithm.

However, I am confused by the optimization algorithms and the concepts of tasks, learning agents and environments.

For example: How would I implement a neural network such as (1) to classify the XOR dataset using a pybrain genetic algorithm (2)?

(1) pybrain.tools.shortcuts.buildNetwork(2, 3, 1)

(2) pybrain.optimization.GA()

Answer 1

In order for our networks to learn anything, we need a dataset that contains inputs and targets. PyBrain has the pybrain. dataset package for this, and we will use the SupervisedDataSet class for our needs.

The SupervisedDataSet class is used for standard supervised learning. It supports input and target values, whose size we have to specify on object creation:

>>> from pybrain. datasets import SupervisedDataSet

>>> ds = SupervisedDataSet(2, 1)

Here we have generated a dataset that supports two-dimensional inputs and one-dimensional targets

Adding samples:

A classic example for neural network training is the XOR function, so let’s just build a dataset for this. We can do this by just adding samples to the dataset:

>>> ds.addSample((0, 0), (0,))

>>> ds.addSample((0, 1), (1,))

>>> ds.addSample((1, 0), (1,))

>>> ds.addSample((1, 1), (0,))

Examining the dataset:

We now have a dataset that has 4 samples in it. We can check that with python’s idiomatic way of checking the size of something:

>>> len(ds)

4

We can also iterate over it in the standard way:

>>> for input, target in ds:

...  print input, target

[ 0.  0.] [ 0.]

[ 0.  1.] [ 1.]

[ 1.  0.] [ 1.]

[ 1.  1.] [ 0.]

We can access the input and target field directly as arrays:

>>> ds['input']

array([[ 0.,  0.],[ 0.,  1.],[ 1.,  0.],[ 1.,  1.]])

>>> ds['target']

array([[ 0.],[ 1.],[ 1.],[ 0.]])

It is also possible to clear a dataset again, and delete all the values from it:

>>> ds.clear()

>>> ds['input']

array([], shape=(0, 2), dtype=float64)

>>> ds['target']

array([], shape=(0, 1), dtype=float64)

To get a more elaborate idea with the algorithms of deep learning refer to our Artificial Intelligence Course.