def buildSimpleGFNN(dim=5, mean=False, conn=False):
N = GFNN('simpleGFNN')
i = LinearLayer(1, name = 'i')
h = GFNNLayer(dim, name = 'gfnn')
if mean:
outdim = 1
else:
outdim = dim
o = LinearLayer(outdim, name = 'o')
N.addOutputModule(o)
N.addInputModule(i)
N.addModule(h)
N.addConnection(FullConnection(i, h, name = 'f1'))
if conn:
N.addRecurrentConnection(FullNotSelfConnection(h, h, name = 'r1'))
if mean:
N.addConnection(RealMeanFieldConnection(h, o, name = 'm1'))
else:
N.addConnection(RealIdentityConnection(h, o, name = 'i1'))
N.sortModules()
return N
def buildGFNN(dim, **options):
opt = {
'name': 'gfnn',
'oscParams': None,
'freqDist': None,
'fs': 40.,
'adaptive': False,
'learnParams': None,
'c0': None,
'outConn': RealIdentityConnection,
'outDim': 1,
}
for key in options:
if key not in opt.keys():
raise NetworkError('buildGFNN unknown option: %s' % key)
opt[key] = options[key]
n = GFNN(opt['name'], fs = opt['fs'])
i = LinearLayer(1, name = 'i')
if opt['adaptive']:
gfnnClass = AFNNLayer
else:
gfnnClass = GFNNLayer
h = gfnnClass(dim,
oscParams = opt['oscParams'], freqDist = opt['freqDist'], name = 'h')
if issubclass(opt['outConn'], RealMeanFieldConnection) or issubclass(opt['outConn'], AbsPhaseIdentityConnection):
outDim = opt['outDim']
else:
outDim = dim
o = LinearLayer(outDim, name = 'o')
n.addInputModule(i)
n.addOutputModule(o)
n.addModule(h)
n.addConnection(GFNNExtConnection(i, h, name = 'ic'))
if opt['learnParams'] is not None:
n.addRecurrentConnection(GFNNIntConnection(h, h,
learnParams = opt['learnParams'], c0 = opt['c0'],
name = 'rc'))
if issubclass(opt['outConn'], RealMeanFieldConnection):
oTo = 0
connPerInter = int(round(float(dim)/outDim))
for x in range(outDim):
hFrom = x * connPerInter
hTo = min(hFrom+connPerInter, dim)
c = RealMeanFieldConnection(h, o, name='gxc'+`x`,
inSliceFrom=hFrom, inSliceTo=hTo,
outSliceFrom=oTo, outSliceTo=oTo+1)
n.addConnection(c)
oTo += 1
else:
n.addConnection(opt['outConn'](h, o, name = 'oc'))
n.sortModules()
return n
def buildGFNNLSTM(gfnnDim, lstmDim, **options):
opt = {
'oscParams': None,
'freqDist': None,
'fs': 40.,
'learnParams': None,
'c0': None,
'interConn': RealMeanFieldConnection,
'gain': 100.0
}
for key in options:
if key not in opt.keys():
raise NetworkError('buildGFNN unknown option: %s' % key)
opt[key] = options[key]
# make GFNN-LSTM - important to use the GFNN class
n = GFNN('gfnn-lstm', fs = opt['fs'])
# input layer
i = LinearLayer(1, name = 'i')
n.addInputModule(i)
# output
o = LinearLayer(1, name = 'o')
n.addOutputModule(o)
# bias
b = BiasUnit(name='b')
n.addModule(b)
# hidden
gfnn = GFNNLayer(gfnnDim, oscParams = opt['oscParams'],
freqDist = opt['freqDist'], name = 'gfnn')
n.addModule(gfnn)
if issubclass(opt['interConn'], MeanFieldConnection):
interDim = 1
else:
interDim = gfnnDim
inter = LinearLayer(interDim, name = 'inter')
n.addModule(inter)
lstm = LSTMLayer(lstmDim, peepholes = True, name = 'lstm')
n.addModule(lstm)
# input -> gfnn
n.addConnection(GFNNExtConnection(i, gfnn, name = 'igc'))
# gfnn -> gfnn
if opt['learnParams'] is not None:
n.addRecurrentConnection(GFNNIntConnection(gfnn, gfnn,
learnParams = opt['learnParams'], c0 = opt['c0'],
name = 'grc'))
# gfnn -> inter
if issubclass(opt['interConn'], MeanFieldConnection):
n.addConnection(opt['interConn'](gfnn, inter, gain = opt['gain'], name = 'gxc'))
else:
n.addConnection(opt['interConn'](gfnn, inter, name = 'gxc'))
# inter -> lstm
n.addConnection(FullConnection(inter, lstm, name = 'xlc'))
# lstm -> lstm
n.addRecurrentConnection(FullConnection(lstm, lstm, name = 'lrc'))
# bias -> lstm
n.addConnection(FullConnection(b, lstm, name = 'blc'))
# lstm -> output
n.addConnection(FullConnection(lstm, o, name = 'loc'))
# bias -> output
n.addConnection(FullConnection(b, lstm))
n.sortModules()
return n
