def __init__(self,
incoming,
num_filters,
filter_size,
stride=(1, 1, 1),
crop=0,
untie_biases=False,
W=lasagne.init.GlorotUniform(),
b=lasagne.init.Constant(0.),
nonlinearity=lasagne.nonlinearities.rectify,
flip_filters=False,
output_size=None,
**kwargs):
# output_size must be set before calling the super constructor
if (not isinstance(output_size, T.Variable)
and output_size is not None):
output_size = as_tuple(output_size, 3, int)
self.output_size = output_size
BaseConvLayer.__init__(self,
incoming,
num_filters,
filter_size,
stride,
crop,
untie_biases,
W,
b,
nonlinearity,
flip_filters,
n=3,
**kwargs)
# rename self.pad to self.crop:
#if crop is None:
self.crop = self.pad
del self.pad
def __init__(self,
incoming,
num_filters,
filter_size,
stride=(1, 1, 1),
pad=0,
untie_biases=False,
W=init.GlorotUniform(),
b=init.Constant(0.),
nonlinearity=nonlinearities.rectify,
flip_filters=True,
convolution=T.nnet.conv3d,
**kwargs):
BaseConvLayer.__init__(self,
incoming,
num_filters,
filter_size,
stride,
pad,
untie_biases,
W,
b,
nonlinearity,
flip_filters,
n=3,
**kwargs)
self.convolution = convolution
def __init__(self, incoming, num_filters, filter_size, stride=(1, 1, 1),
pad=0, untie_biases=False,
W=init.GlorotUniform(), b=init.Constant(0.),
nonlinearity=nonlinearities.rectify, flip_filters=True,
convolution=T.nnet.conv3d, **kwargs):
BaseConvLayer.__init__(self, incoming, num_filters, filter_size,
stride, pad, untie_biases, W, b,
nonlinearity, flip_filters, n=3,
**kwargs)
self.convolution = convolution