def get_layers(self):
in_l = InputLayer((self.n_examples, self.n_time_steps, self.n_chans))
in_bandpass = InputLayer(
(self.n_examples, self.n_time_steps, self.n_chans, self.n_filters))
l_bandpass = BandpassLayer([in_l, in_bandpass],
n_filt_order=self.n_filt_order,
truncate_gradient=self.truncate_gradient)
# out comes examples x timesteps x chans x filters
l_spat_filt = TensorDotLayer(l_bandpass,
n_filters=self.n_spat_filters,
axis=2)
# still examples x timesteps x chans x filters
l_square = NonlinearityLayer(l_spat_filt, T.sqr)
# now adding empty chan dim so we can make pooling per output chan
l_shape_pad = DimshuffleLayer(l_square, (0, 'x', 1, 2, 3))
# examples x convchans x timesteps x chans x filters
l_pooled = Pool3DDNNLayer(l_shape_pad,
pool_size=(self.n_pool_len, 1, 1),
stride=1,
mode='average_exc_pad')
l_log = NonlinearityLayer(l_pooled, safe_log)
# removing empty convchan dim again
l_sliced = SliceLayer(l_log, indices=0, axis=1)
# now examples x timesteps x chans x filters
l_flat = FlattenLayer(l_sliced, outdim=3)
# now examples x timesteps x features (chans * filters)
l_dense = TensorDotLayer(l_flat, n_filters=1, axis=2)
# now examples x timesteps x 1
l_nonlin = NonlinearityLayer(l_dense, sigmoid)
return lasagne.layers.get_all_layers(l_nonlin)
def layer(self):
from lasagne.layers.shape import FlattenLayer
return FlattenLayer(Mock(output_shape=(None, )))
def test_dim0_raises(self):
from lasagne.layers.shape import FlattenLayer
with pytest.raises(ValueError):
FlattenLayer((2, 3, 4), outdim=0)
def layer_outdim1(self):
from lasagne.layers.shape import FlattenLayer
return FlattenLayer(Mock(output_shapes=((None, ), )), outdim=1)