def __init__(self, size_in, length, size, stride=1, padding=None):
super(Convolution1D, self).__init__()
util.autoassign(locals())
padding = padding if padding is not None else self.length
self.Conv = nn.Conv1d(self.size_in, self.size, self.length, stride=self.stride, padding=padding, bias=False)
# use Glorot uniform initialization
self.Conv.weight.data = init.glorot_uniform((self.size, self.size_in, self.length, 1)).squeeze()
def __init__(self, size_in, length, size, stride=1):
super(Convolution1D, self).__init__()
util.autoassign(locals())
self.Conv = nn.Conv1d(self.size_in,
self.size,
self.length,
stride=self.stride,
padding=self.length)
# use Glorot uniform initialization
self.Conv.weight.data = init.glorot_uniform(
(self.size, self.size_in, self.length))
def __init__(self,
size_in,
length,
size,
stride=1,
padding=None,
maxpool=False):
super(Convolution1D, self).__init__()
util.autoassign(locals())
padding = padding if padding is not None else self.length
self.Conv = nn.Conv1d(self.size_in,
self.size,
self.length,
stride=self.stride,
padding=padding,
bias=False)
# use Glorot uniform initialization
self.Conv.weight.data = init.glorot_uniform(
(self.size, self.size_in, self.length, 1)).squeeze()
#FIXME what is the correct padding???
if self.maxpool:
self.Maxpool = nn.MaxPool1d(2, 2, ceil_mode=True)