def __init__(self, inputdim, insize, convSize, numFeatureMaps, **args):
FeedForwardNetwork.__init__(self, **args)
inlayer = LinearLayer(inputdim * insize * insize)
self.addInputModule(inlayer)
self._buildStructure(inputdim, insize, inlayer, convSize,
numFeatureMaps)
self.sortModules()
def __init__(self, predefined = None, **kwargs):
""" For the current implementation, the sequence length
needs to be fixed, and given at construction time. """
if predefined is not None:
self.predefined = predefined
else:
self.predefined = {}
FeedForwardNetwork.__init__(self, **kwargs)
assert self.seqlen is not None
# the input is a 1D-mesh (as a view on a flat input layer)
inmod = LinearLayer(self.inputsize * self.seqlen, name='input')
inmesh = ModuleMesh.viewOnFlatLayer(inmod, (self.seqlen,), 'inmesh')
# the output is also a 1D-mesh
outmod = self.outcomponentclass(self.outputsize * self.seqlen, name='output')
outmesh = ModuleMesh.viewOnFlatLayer(outmod, (self.seqlen,), 'outmesh')
# the hidden layers are places in a 2xseqlen mesh
hiddenmesh = ModuleMesh.constructWithLayers(self.componentclass, self.hiddensize,
(2, self.seqlen), 'hidden')
# add the modules
for c in inmesh:
self.addInputModule(c)
for c in outmesh:
self.addOutputModule(c)
for c in hiddenmesh:
self.addModule(c)
# set the connections weights to be shared
inconnf = MotherConnection(inmesh.componentOutdim * hiddenmesh.componentIndim, name='inconn')
outconnf = MotherConnection(outmesh.componentIndim * hiddenmesh.componentOutdim, name='outconn')
forwardconn = MotherConnection(hiddenmesh.componentIndim * hiddenmesh.componentOutdim, name='fconn')
if self.symmetric:
backwardconn = forwardconn
inconnb = inconnf
outconnb = outconnf
else:
backwardconn = MotherConnection(hiddenmesh.componentIndim * hiddenmesh.componentOutdim, name='bconn')
inconnb = MotherConnection(inmesh.componentOutdim * hiddenmesh.componentIndim, name='inconn')
outconnb = MotherConnection(outmesh.componentIndim * hiddenmesh.componentOutdim, name='outconn')
# build the connections
for i in range(self.seqlen):
# input to hidden
self.addConnection(SharedFullConnection(inconnf, inmesh[(i,)], hiddenmesh[(0, i)]))
self.addConnection(SharedFullConnection(inconnb, inmesh[(i,)], hiddenmesh[(1, i)]))
# hidden to output
self.addConnection(SharedFullConnection(outconnf, hiddenmesh[(0, i)], outmesh[(i,)]))
self.addConnection(SharedFullConnection(outconnb, hiddenmesh[(1, i)], outmesh[(i,)]))
if i > 0:
# forward in time
self.addConnection(SharedFullConnection(forwardconn, hiddenmesh[(0, i - 1)], hiddenmesh[(0, i)]))
if i < self.seqlen - 1:
# backward in time
self.addConnection(SharedFullConnection(backwardconn, hiddenmesh[(1, i + 1)], hiddenmesh[(1, i)]))
self.sortModules()
def __init__(self, predefined = None, **kwargs):
""" For the current implementation, the sequence length
needs to be fixed, and given at construction time. """
if predefined is not None:
self.predefined = predefined
else:
self.predefined = {}
FeedForwardNetwork.__init__(self, **kwargs)
assert self.seqlen is not None
# the input is a 1D-mesh (as a view on a flat input layer)
inmod = LinearLayer(self.inputsize * self.seqlen, name='input')
inmesh = ModuleMesh.viewOnFlatLayer(inmod, (self.seqlen,), 'inmesh')
# the output is also a 1D-mesh
outmod = self.outcomponentclass(self.outputsize * self.seqlen, name='output')
outmesh = ModuleMesh.viewOnFlatLayer(outmod, (self.seqlen,), 'outmesh')
# the hidden layers are places in a 2xseqlen mesh
hiddenmesh = ModuleMesh.constructWithLayers(self.componentclass, self.hiddensize,
(2, self.seqlen), 'hidden')
# add the modules
for c in inmesh:
self.addInputModule(c)
for c in outmesh:
self.addOutputModule(c)
for c in hiddenmesh:
self.addModule(c)
# set the connections weights to be shared
inconnf = MotherConnection(inmesh.componentOutdim * hiddenmesh.componentIndim, name='inconn')
outconnf = MotherConnection(outmesh.componentIndim * hiddenmesh.componentOutdim, name='outconn')
forwardconn = MotherConnection(hiddenmesh.componentIndim * hiddenmesh.componentOutdim, name='fconn')
if self.symmetric:
backwardconn = forwardconn
inconnb = inconnf
outconnb = outconnf
else:
backwardconn = MotherConnection(hiddenmesh.componentIndim * hiddenmesh.componentOutdim, name='bconn')
inconnb = MotherConnection(inmesh.componentOutdim * hiddenmesh.componentIndim, name='inconn')
outconnb = MotherConnection(outmesh.componentIndim * hiddenmesh.componentOutdim, name='outconn')
# build the connections
for i in range(self.seqlen):
# input to hidden
self.addConnection(SharedFullConnection(inconnf, inmesh[(i,)], hiddenmesh[(0, i)]))
self.addConnection(SharedFullConnection(inconnb, inmesh[(i,)], hiddenmesh[(1, i)]))
# hidden to output
self.addConnection(SharedFullConnection(outconnf, hiddenmesh[(0, i)], outmesh[(i,)]))
self.addConnection(SharedFullConnection(outconnb, hiddenmesh[(1, i)], outmesh[(i,)]))
if i > 0:
# forward in time
self.addConnection(SharedFullConnection(forwardconn, hiddenmesh[(0, i - 1)], hiddenmesh[(0, i)]))
if i < self.seqlen - 1:
# backward in time
self.addConnection(SharedFullConnection(backwardconn, hiddenmesh[(1, i + 1)], hiddenmesh[(1, i)]))
self.sortModules()
def __init__(self, boardSize, convSize, numFeatureMaps, **args):
inputdim = 2
FeedForwardNetwork.__init__(self, **args)
inlayer = LinearLayer(inputdim * boardSize * boardSize, name='in')
self.addInputModule(inlayer)
# we need some treatment of the border too - thus we pad the direct board input.
x = convSize / 2
insize = boardSize + 2 * x
if convSize % 2 == 0:
insize -= 1
paddedlayer = LinearLayer(inputdim * insize * insize, name='pad')
self.addModule(paddedlayer)
# we connect a bias to the padded-parts (with shared but trainable weights).
bias = BiasUnit()
self.addModule(bias)
biasConn = MotherConnection(inputdim)
paddable = []
if convSize % 2 == 0:
xs = range(x) + range(insize - x + 1, insize)
else:
xs = range(x) + range(insize - x, insize)
paddable.extend(crossproduct([range(insize), xs]))
paddable.extend(crossproduct([xs, range(x, boardSize + x)]))
for (i, j) in paddable:
self.addConnection(
SharedFullConnection(biasConn,
bias,
paddedlayer,
outSliceFrom=(i * insize + j) * inputdim,
outSliceTo=(i * insize + j + 1) *
inputdim))
for i in range(boardSize):
inmod = ModuleSlice(inlayer,
outSliceFrom=i * boardSize * inputdim,
outSliceTo=(i + 1) * boardSize * inputdim)
outmod = ModuleSlice(paddedlayer,
inSliceFrom=((i + x) * insize + x) * inputdim,
inSliceTo=((i + x) * insize + x + boardSize) *
inputdim)
self.addConnection(IdentityConnection(inmod, outmod))
self._buildStructure(inputdim, insize, paddedlayer, convSize,
numFeatureMaps)
self.sortModules()
def __init__(self, boardSize, convSize, numFeatureMaps, **args):
inputdim = 2
FeedForwardNetwork.__init__(self, **args)
inlayer = LinearLayer(inputdim*boardSize*boardSize, name = 'in')
self.addInputModule(inlayer)
# we need some treatment of the border too - thus we pad the direct board input.
x = convSize/2
insize = boardSize+2*x
if convSize % 2 == 0:
insize -= 1
paddedlayer = LinearLayer(inputdim*insize*insize, name = 'pad')
self.addModule(paddedlayer)
# we connect a bias to the padded-parts (with shared but trainable weights).
bias = BiasUnit()
self.addModule(bias)
biasConn = MotherConnection(inputdim)
paddable = []
if convSize % 2 == 0:
xs = range(x)+range(insize-x+1, insize)
else:
xs = range(x)+range(insize-x, insize)
paddable.extend(crossproduct([range(insize), xs]))
paddable.extend(crossproduct([xs, range(x, boardSize+x)]))
for (i, j) in paddable:
self.addConnection(SharedFullConnection(biasConn, bias, paddedlayer,
outSliceFrom = (i*insize+j)*inputdim,
outSliceTo = (i*insize+j+1)*inputdim))
for i in range(boardSize):
inmod = ModuleSlice(inlayer, outSliceFrom = i*boardSize*inputdim,
outSliceTo = (i+1)*boardSize*inputdim)
outmod = ModuleSlice(paddedlayer, inSliceFrom = ((i+x)*insize+x)*inputdim,
inSliceTo = ((i+x)*insize+x+boardSize)*inputdim)
self.addConnection(IdentityConnection(inmod, outmod))
self._buildStructure(inputdim, insize, paddedlayer, convSize, numFeatureMaps)
self.sortModules()
def __init__(self, inputdim, insize, convSize, numFeatureMaps, **args):
FeedForwardNetwork.__init__(self, **args)
inlayer = LinearLayer(inputdim * insize * insize)
self.addInputModule(inlayer)
self._buildStructure(inputdim, insize, inlayer, convSize, numFeatureMaps)
self.sortModules()
def __init__(self, **args):
FeedForwardNetwork.__init__(self, **args)
