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 test_convolve_not_implemented(self):
from lasagne.layers.conv import BaseConvLayer
layer = BaseConvLayer((10, 20, 30), 1, 3)
with pytest.raises(NotImplementedError):
layer.convolve(theano.tensor.tensor3())
def test_infer_dimensionality(self):
from lasagne.layers.conv import BaseConvLayer
shape = (10, 20, 30, 40, 50, 60)
for n in range(1, 4):
layer = BaseConvLayer(shape[:n + 2], 1, 3)
assert layer.n == n
def test_convolve_not_implemented(self):
from lasagne.layers.conv import BaseConvLayer
layer = BaseConvLayer((10, 20, 30), 1, 3)
with pytest.raises(NotImplementedError):
layer.convolve(theano.tensor.tensor3())