def get_layers(self):
# make into list if nonlin only one
if not hasattr(self.nonlin_before_merge, '__len__'):
nonlins_before_merge = ((self.nonlin_before_merge,) *
len(self.networks))
else:
nonlins_before_merge = self.nonlin_before_merge
layers_per_net = [net.get_layers() for net in self.networks]
# Check that all have same number of sample preds
n_sample_preds = get_n_sample_preds(layers_per_net[0][-1])
for layers in layers_per_net:
assert get_n_sample_preds(layers[-1]) == n_sample_preds
# remove dense softmax replace by dense linear
reduced_layers = [replace_dense_softmax_by_dense_linear(all_l, n_f,
nonlin_before_merge=nonlin,
batch_norm_before_merge=self.batch_norm_before_merge)
for all_l, n_f, nonlin in zip(layers_per_net, self.n_features_per_net,
nonlins_before_merge)]
# hopefully still works with new method below:)
use_same_input_layer(reduced_layers)
final_layers = [layers[-1] for layers in reduced_layers]
l_merged = ConcatLayer(final_layers)
l_merged = DenseLayer(l_merged,num_units=self.n_classes,
nonlinearity=softmax)
return lasagne.layers.get_all_layers(l_merged)
def crop_layer_1(self):
from lasagne.layers.merge import ConcatLayer
return ConcatLayer([Mock(), Mock()], axis=1,
cropping=['lower'] * 2)
def layer(self):
from lasagne.layers.merge import ConcatLayer
return ConcatLayer([Mock(), Mock()], axis=1)
def crop_layer_1(self):
from lasagne.layers.merge import ConcatLayer
l1 = Mock(output_shapes=((None, None), ))
l2 = Mock(output_shapes=((None, None), ))
return ConcatLayer((l1, l2), axis=1, cropping=['lower'] * 2)
def layer(self, axis):
from lasagne.layers.merge import ConcatLayer
l1 = Mock(output_shapes=((None, None), ))
l2 = Mock(output_shapes=((None, None), ))
return ConcatLayer((l1, l2), axis=axis)
