def __init__(self):
self.width = 28
self.height = 28
self.testds, self.trainds = \
makeMnistDataSets('/Users/bayerj/Desktop/MNIST/')
# Initialize MDRNN
self.net = _FeedForwardNetwork()
inlayer = LinearLayer(self.width * self.height)
hiddenlayer = MdrnnLayer(timedim=2,
shape=(self.width, self.height),
blockshape=(1, 1),
hiddendim=4,
outsize=10,
name='mdrnn')
outlayer = SigmoidLayer(self.width * self.height * 10)
con1 = IdentityConnection(inlayer, hiddenlayer)
con2 = IdentityConnection(hiddenlayer, outlayer)
self.net.addInputModule(inlayer)
self.net.addModule(hiddenlayer)
self.net.addOutputModule(outlayer)
self.net.addConnection(con1)
self.net.addConnection(con2)
self.net.sortModules()
__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)