__author__ = 'Justin S Bayer, [email protected]' __version__ = '$Id$' import scipy from pybrain.datasets import UnsupervisedDataSet from pybrain.unsupervised.trainers.deepbelief import DeepBeliefTrainer from pybrain.tools.shortcuts import buildNetwork from pybrainexamples.datasets.mnist import makeMnistDataSets net = buildNetwork(784, 500, 500, 2000, bias=True) train, test = makeMnistDataSets('/Users/bayerj/Desktop/MNIST/') trainer = DeepBeliefTrainer(net, train) trainer.train() print "RBM Phase finished. Now backprop." softmaxer = SoftmaxLayer(10) con = FullConnection(net.outmodules[0], softmaxer) net.addModule(softmaxer) net.outmodules = [softmaxer] trainer = BackpropTrainer(trainer, ds) for i in xrange(sys.maxint): error = trainer.train() print "%i: %.2f" % (i, error)
#! /usr/bin/env python2.5 # -*- coding: utf-8 -*- # Miniscule deep belief net example __author__ = 'Justin S Bayer, [email protected]' __version__ = '$Id$' from pybrain.datasets import UnsupervisedDataSet from pybrain.unsupervised.trainers.deepbelief import DeepBeliefTrainer from pybrain.tools.shortcuts import buildNetwork ds = UnsupervisedDataSet(6) ds.addSample([0, 1] * 3) ds.addSample([1, 0] * 3) net = buildNetwork(6, 2, 2, 2, bias=True) params = net.params.copy() trainer = DeepBeliefTrainer(net, ds) trainer.train() print params == net.params
