def nn_routine_ds(ds):
feasize = ds['input'].shape[1]
trainDS, testDS = ds.splitWithProportion(0.8)
net = _FeedForwardNetwork()
inLayer = LinearLayer(feasize)
net.addInputModule(inLayer)
net, hl = add_fullyC_layer(net, inLayer, TanhLayer(feasize*8))
net, hl = add_fullyC_layer(net, hl, TanhLayer(feasize*4))
net, hl = add_fullyC_layer(net, hl, TanhLayer(feasize*2))
net, hl = add_fullyC_layer(net, hl, TanhLayer(feasize))
net, hl = add_fullyC_layer(net, hl, TanhLayer(feasize/2))
net, hl = add_fullyC_layer(net, hl, TanhLayer(feasize/4))
net, hl = add_fullyC_layer(net, hl, TanhLayer(feasize/8))
outLayer = SigmoidLayer(1)
net.addOutputModule(outLayer)
hidden_last_to_out = FullConnection(hl, outLayer)
net.addConnection(hidden_last_to_out)
net.sortModules()
trainer = BackpropTrainer(net, trainDS, learningrate=0.001, momentum=.1, verbose=True)
epochs = 10000
for zzz in range(epochs):
if zzz % 100 == 0:
print "%2.3f percent complete" % (100.*zzz/epochs)
trainer.train()
res = np.append(testDS['target'], net.activateOnDataset(testDS), axis=1)
return (net, trainer, trainDS, testDS, res)
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()
def testParametersDerivatives(self):
rnet = pybrainbridge._RecurrentNetwork()
self.lstm_network(rnet)
rnet.sortModules()
self.assert_(getattr(rnet, '_derivs', None) is not None)
fnet = pybrainbridge._FeedForwardNetwork()
self.two_layer_network(fnet)
fnet.sortModules()
self.assert_(getattr(fnet, '_derivs', None) is not None)
def testParametersDerivatives(self):
rnet = pybrainbridge._RecurrentNetwork()
self.lstm_network(rnet)
rnet.sortModules()
self.assert_(getattr(rnet, '_derivs', None) is not None)
fnet = pybrainbridge._FeedForwardNetwork()
self.two_layer_network(fnet)
fnet.sortModules()
self.assert_(getattr(fnet, '_derivs', None) is not None)
def convertToFastNetwork(self):
""" Attempt to transform the network into a fast network. If fast networks are not available,
or the network cannot be converted, it returns None. """
from pybrain.structure.networks import FeedForwardNetwork, RecurrentNetwork
try:
from arac.pybrainbridge import _RecurrentNetwork, _FeedForwardNetwork # @UnresolvedImport
except:
print "No fast networks available."
return None
net = self.copy()
if isinstance(net, FeedForwardNetwork):
cnet = _FeedForwardNetwork()
elif isinstance(net, RecurrentNetwork):
cnet = _RecurrentNetwork()
for m in net.inmodules:
cnet.addInputModule(m)
for m in net.outmodules:
cnet.addOutputModule(m)
for m in net.modules:
cnet.addModule(m)
for clist in net.connections.values():
for c in clist:
cnet.addConnection(c)
if isinstance(net, RecurrentNetwork):
for c in net.recurrentConns:
cnet.addRecurrentConnection(c)
try:
cnet.sortModules()
except ValueError:
print "Network cannot be converted."
return None
cnet.owner = cnet
return cnet
def convertToFastNetwork(self):
""" Attempt to transform the network into a fast network. If fast networks are not available,
or the network cannot be converted, it returns None. """
from pybrain.structure.networks import FeedForwardNetwork, RecurrentNetwork
try:
from arac.pybrainbridge import _RecurrentNetwork, _FeedForwardNetwork #@UnresolvedImport
except:
print("No fast networks available.")
return None
net = self.copy()
if isinstance(net, FeedForwardNetwork):
cnet = _FeedForwardNetwork()
elif isinstance(net, RecurrentNetwork):
cnet = _RecurrentNetwork()
for m in net.inmodules:
cnet.addInputModule(m)
for m in net.outmodules:
cnet.addOutputModule(m)
for m in net.modules:
cnet.addModule(m)
for clist in list(net.connections.values()):
for c in clist:
cnet.addConnection(c)
if isinstance(net, RecurrentNetwork):
for c in net.recurrentConns:
cnet.addRecurrentConnection(c)
try:
cnet.sortModules()
except ValueError:
print("Network cannot be converted.")
return None
cnet.owner = cnet
return cnet
def equivalence_feed_forward(self, builder):
_net = pybrainbridge._FeedForwardNetwork()
builder(_net)
net = FeedForwardNetwork()
builder(net)
super(TestNetworkEquivalence, self).equivalence_feed_forward(net, _net)
def equivalence_feed_forward(self, builder):
_net = pybrainbridge._FeedForwardNetwork()
builder(_net)
net = FeedForwardNetwork()
builder(net)
super(TestNetworkEquivalence, self).equivalence_feed_forward(net, _net)