xbob.mlp.lbfgs 0.1d

MLP L-BFGS Trainer for Bob

This example demonstrates how to extend Bob by providing a new L-BFGS-based trainer for the multilayer perceptron (MLP) implementation of Bob.

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.

>>> 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)