L-BFGS-based trainer for the MLP machine of Bob
Project description
This example demonstrates how to extend Bob by providing a new L-BFGS-based trainer for the multilayer perceptron (MLP) implementation of Bob.
Installation
First, you have to install bob following the instructions there.
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.
Using zc.buildout
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.
User Guide
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)
