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)