def __init__(
self,
numFilters,
filterHeight,
filterWidth,
filterDepth,
name,
stride=(1, 1, 1),
pad=0,
untieBiases=False,
initializations=[MI.GlorotNormal('W'),
MI.SingleValue('b', 0)],
flipFilters=True,
**kwargs):
super(Convolution3D,
self).__init__(LasagneCONV.Conv3DLayer,
initializations=initializations,
lasagneHyperParameters={
"numFilters":
numFilters,
"filter_size":
(filterHeight, filterWidth, filterDepth),
"stride":
stride,
"pad":
pad,
"untie_biases":
untieBiases,
"flip_filters":
flipFilters
},
lasagneKwargs={},
name=name,
**kwargs)
def __init__(
self,
size,
initializations=[MI.GlorotNormal('W'),
MI.SingleValue('b', 0)],
**kwargs):
super(Dense, self).__init__(initializations=initializations, **kwargs)
if isinstance(size, int):
sh = (None, size)
elif isinstance(size, float):
sh = (None, int(size))
else:
sh = [None]
sh.extend(list(size))
sh = tuple(sh)
self.size = size
self.setHP("shape", sh)
self.setParameters({
"W": MTYPES.Parameter("%s.W" % (self.name)),
"b": MTYPES.Parameter("%s.b" % (self.name))
})
self.inputShape = None
self.originalInputShape = None
def __init__(
self,
numFilters,
filterSize,
name,
stride=1,
pad=0,
untieBiases=False,
flipFilters=True,
initializations=[MI.GlorotNormal('W'),
MI.SingleValue('b', 0)],
**kwargs):
super(Convolution1D, self).__init__(LasagneCONV.Conv1DLayer,
initializations=initializations,
lasagneHyperParameters={
"num_filters": numFilters,
"filter_size": filterSize,
"stride": stride,
"pad": pad,
"untie_biases": untieBiases,
"flip_filters": flipFilters
},
lasagneKwargs={},
**kwargs)