def buildMinimalLSTMNetwork():
N = RecurrentNetwork('simpleLstmNet')
i = LinearLayer(4, name='i')
h = LSTMLayer(1, peepholes=True, name='lstm')
o = LinearLayer(1, name='o')
N.addInputModule(i)
N.addModule(h)
N.addOutputModule(o)
N.addConnection(IdentityConnection(i, h))
N.addConnection(IdentityConnection(h, o))
N.sortModules()
return N
def buildMinimalMDLSTMNetwork():
N = RecurrentNetwork('simpleMdLstmNet')
i = LinearLayer(4, name='i')
h = MDLSTMLayer(1, peepholes=True, name='mdlstm')
o = LinearLayer(1, name='o')
N.addInputModule(i)
N.addModule(h)
N.addOutputModule(o)
N.addConnection(IdentityConnection(i, h, outSliceTo=4))
N.addRecurrentConnection(
IdentityConnection(h, h, outSliceFrom=4, inSliceFrom=1))
N.addConnection(IdentityConnection(h, o, inSliceTo=1))
N.sortModules()
return N
def buildNonGravityNet(recurrent = False):
if recurrent:
net = RecurrentNetwork()
else:
net = FeedForwardNetwork()
l1 = LinearLayer(2)
l2 = LinearLayer(3)
s1 = SigmoidLayer(2)
l3 = LinearLayer(1)
net.addInputModule(l1)
net.addModule(l2)
net.addModule(s1)
net.addOutputModule(l3)
net.addConnection(IdentityConnection(l1, l2, outSliceFrom = 1))
net.addConnection(IdentityConnection(l1, l2, outSliceTo = 2))
net.addConnection(IdentityConnection(l2, l3, inSliceFrom = 2))
net.addConnection(IdentityConnection(l2, l3, inSliceTo = 1))
net.addConnection(IdentityConnection(l1, s1))
net.addConnection(IdentityConnection(l2, s1, inSliceFrom = 1))
net.addConnection(IdentityConnection(s1, l3, inSliceFrom = 1))
if recurrent:
net.addRecurrentConnection(IdentityConnection(s1, l1))
net.addRecurrentConnection(IdentityConnection(l2, l2, inSliceFrom = 1, outSliceTo = 2))
net.sortModules()
return net
#!/usr/bin/python from pybrain3.structure import TanhLayer from pybrain3.structure import FeedForwardNetwork, LinearLayer, SigmoidLayer, FullConnection, RecurrentNetwork from pybrain3.structure.modules.biasunit import BiasUnit # network = FeedForwardNetwork() network = RecurrentNetwork() inLayer = LinearLayer(35) # hiddenLayer = SigmoidLayer(5) hiddenLayer = TanhLayer(5) outLayer = LinearLayer(1) bias = BiasUnit() network.addInputModule(inLayer) network.addModule(bias) network.addModule(hiddenLayer) network.addOutputModule(outLayer) in_to_hidden = FullConnection(inLayer, hiddenLayer) bias_to_hidden = FullConnection(bias, hiddenLayer) hidden_to_out = FullConnection(hiddenLayer, outLayer) network.addConnection(in_to_hidden) network.addConnection(bias_to_hidden) network.addConnection(hidden_to_out) network.sortModules()
def buildMixedNestedNetwork():
""" build a nested network with the inner one being a ffn and the outer one being recurrent. """
N = RecurrentNetwork('outer')
a = LinearLayer(1, name='a')
b = LinearLayer(2, name='b')
c = buildNetwork(2, 3, 1)
c.name = 'inner'
N.addInputModule(a)
N.addModule(c)
N.addOutputModule(b)
N.addConnection(FullConnection(a, b))
N.addConnection(FullConnection(b, c))
N.addRecurrentConnection(FullConnection(c, c))
N.sortModules()
return N