def buildSharedCrossedNetwork():
""" build a network with shared connections. Two hidden modules are
symmetrically 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 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 _buildCaptureNetwork(self):
# the input is a 2D-mesh (as a view on a flat input layer)
inmod = LinearLayer(self.insize * self.size * self.size, name='input')
inmesh = ModuleMesh.viewOnFlatLayer(inmod, (self.size, self.size),
'inmesh')
# the output is a 2D-mesh (as a view on a flat sigmoid output layer)
outmod = self.outcomponentclass(self.outputs * self.size * self.size,
name='output')
outmesh = ModuleMesh.viewOnFlatLayer(outmod, (self.size, self.size),
'outmesh')
if self.componentclass is MDLSTMLayer:
c = lambda: MDLSTMLayer(self.hsize, 2, self.peepholes).meatSlice()
hiddenmesh = ModuleMesh(c, (self.size, self.size, 4),
'hidden',
baserename=True)
else:
hiddenmesh = ModuleMesh.constructWithLayers(
self.componentclass, self.hsize, (self.size, self.size, 4),
'hidden')
self._buildBorderStructure(inmesh, hiddenmesh, outmesh)
# add the identity connections for the states
for m in self.modules:
if isinstance(m, MDLSTMLayer):
tmp = m.stateSlice()
index = 0
for c in list(self.connections[m]):
if isinstance(c.outmod, MDLSTMLayer):
self.addConnection(
IdentityConnection(
tmp,
c.outmod.stateSlice(),
outSliceFrom=self.hsize * (index),
outSliceTo=self.hsize * (index + 1)))
index += 1
# direct connections between input and output
if self.directlink:
self._buildDirectLink(inmesh, outmesh)
# combined inputs
if self.combinputs > 0:
cin = LinearLayer(self.combinputs, name='globalin')
self.addInputModule(cin)
if 'globalinconn' not in self.predefined:
self.predefined['globalinconn'] = MotherConnection(
cin.componentOutdim * hiddenmesh.componentIndim,
'globalinconn')
self._linkToAll(cin, hiddenmesh, self.predefined['globalinconn'])