def buildSharedCrossedNetwork():
""" build a network with shared connections. Two hiddne modules are symetrically linked, but to a different
input neuron than the output neuron. The weights are random. """
N = FeedForwardNetwork('shared-crossed')
h = 1
a = LinearLayer(2, name='a')
b = LinearLayer(h, name='b')
c = LinearLayer(h, name='c')
d = LinearLayer(2, name='d')
N.addInputModule(a)
N.addModule(b)
N.addModule(c)
N.addOutputModule(d)
m1 = MotherConnection(h)
m1.params[:] = scipy.array((1, ))
m2 = MotherConnection(h)
m2.params[:] = scipy.array((2, ))
N.addConnection(SharedFullConnection(m1, a, b, inSliceTo=1))
N.addConnection(SharedFullConnection(m1, a, c, inSliceFrom=1))
N.addConnection(SharedFullConnection(m2, b, d, outSliceFrom=1))
N.addConnection(SharedFullConnection(m2, c, d, outSliceTo=1))
N.sortModules()
return N
def _buildDirectLink(self, inmesh, outmesh):
if not 'directconn' in self.predefined:
self.predefined['directconn'] = MotherConnection(
inmesh.componentOutdim * outmesh.componentIndim, 'inconn')
for unit in self._iterateOverUnits():
self.addConnection(
SharedFullConnection(self.predefined['directconn'],
inmesh[unit], outmesh[unit]))
def _linkToAll(self, inmod, mesh, conn):
for unit in self._iterateOverUnits():
self.addConnection(SharedFullConnection(conn, inmod, mesh[unit]))