L-BFGS-based trainer for the MLP machine of Bob
This example demonstrates how to extend Bob by providing a new L-BFGS-based trainer for the multilayer perceptron (MLP) implementation of Bob.
If you are reading this page through our GitHub portal and not through PyPI, note the development tip of the package may not be stable or become unstable in a matter of moments.
Go to http://pypi.python.org/pypi/xbob.mlp.lbfgs to download the latest stable version of this package.
There are two options you can follow to get this package installed and operational on your computer: you can use automatic installers like pip (or easy_install) or manually download, unpack and use zc.buildout to create a virtual work environment just for this package. In both cases, the two dependences listed above will be automatically downloaded and installed.
Using an automatic installer
Using pip is the easiest (shell commands are marked with a $ signal):
$ pip install xbob.mlp.lbfgs
You can also do the same with easy_install:
$ easy_install xbob.mlp.lbfgs
This will download and install this package plus any other required dependencies. It will also verify if the version of Bob you have installed is compatible.
This scheme works well with virtual environments by virtualenv or if you have root access to your machine. Otherwise, we recommend you use the next option.
Download the latest version of this package from PyPI and unpack it in your working area. The installation of the toolkit itself uses buildout. You don’t need to understand its inner workings to use this package. Here is a recipe to get you started:
$ python bootstrap.py $ ./bin/buildout
These two commands should download and install all non-installed dependencies and get you a fully operational test and development environment.
The python shell used in the first line of the previous command set determines the python interpreter that will be used for all scripts developed inside this package. Because this package makes use of Bob, you must make sure that the bootstrap.py script is called with the same interpreter used to build Bob, or unexpected problems might occur.
If Bob is installed by the administrator of your system, it is safe to consider it uses the default python interpreter. In this case, the above 3 command lines should work as expected. If you have Bob installed somewhere else on a private directory, edit the file buildout.cfg before running ./bin/buildout. Find the section named buildout and edit or add the line prefixes to point to the directory where Bob is installed or built. For example:
[buildout] ... prefixes=/Users/crazyfox/work/bob/build
It is assumed you have followed the installation instructions for the package and got this package installed.
Below, we provide an example of how to train an MLP using this trainer, from the python universe:
>>> machine = bob.machine.MLP((n_inputs, n_hidden, n_outputs)) >>> # Initialize the machine weights/biases as wished >>> trainer = xbob.mlp.lbfgs.Trainer(1e-6) >>> trainer.initialize(machine) >>> trainer.train(machine, X, labels)
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