def build_autoencoder_network():
input_var = T.tensor4('input_var');
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(1,1), stride=1, pad='same', nonlinearity=leaky_rectify));
pool1 = layers.MaxPool2DLayer(layer, (2, 2), 2);
layer = batch_norm(layers.Conv2DLayer(pool1, 240, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 320, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 320, filter_size=(1,1), stride=1, pad='same', nonlinearity=leaky_rectify));
pool2 = layers.MaxPool2DLayer(layer, (2, 2), 2);
layer = batch_norm(layers.Conv2DLayer(pool2, 640, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
prely = batch_norm(layers.Conv2DLayer(layer, 1024, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
featm = batch_norm(layers.Conv2DLayer(prely, 640, filter_size=(1,1), nonlinearity=leaky_rectify));
feat_map = batch_norm(layers.Conv2DLayer(featm, 100, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
maskm = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
mask_rep = batch_norm(layers.Conv2DLayer(maskm, 1, filter_size=(1,1), nonlinearity=None), beta=None, gamma=None);
mask_map = SoftThresPerc(mask_rep, perc=90.0, alpha=0.1, beta=init.Constant(0.5), tight=100.0, name="mask_map");
layer = ChInnerProdMerge(feat_map, mask_map, name="encoder");
layer = batch_norm(layers.Deconv2DLayer(layer, 1024, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 640, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(1,1), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.InverseLayer(layer, pool2);
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(1,1), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.InverseLayer(layer, pool1);
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.Deconv2DLayer(layer, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
glblf = batch_norm(layers.Conv2DLayer(prely, 128, filter_size=(1,1), nonlinearity=leaky_rectify));
glblf = layers.Pool2DLayer(glblf, pool_size=(5,5), stride=5, mode='average_inc_pad');
glblf = batch_norm(layers.Conv2DLayer(glblf, 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Conv2DLayer(glblf, 5, filter_size=(1,1), nonlinearity=rectify), name="global_feature");
glblf = batch_norm(layers.Deconv2DLayer(glblf, 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(9,9), stride=5, crop=(2,2), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Deconv2DLayer(glblf, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
layer = layers.ElemwiseSumLayer([layer, glblf]);
network = ReshapeLayer(layer, ([0], -1));
mask_var = lasagne.layers.get_output(mask_map);
output_var = lasagne.layers.get_output(network);
return network, input_var, mask_var, output_var;
def build_network_from_ae(classn):
input_var = T.tensor4('input_var')
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var)
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
120,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = layers.Pool2DLayer(layer,
pool_size=(2, 2),
stride=2,
mode='average_inc_pad')
layer = batch_norm(
layers.Conv2DLayer(layer,
240,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = layers.Pool2DLayer(layer,
pool_size=(2, 2),
stride=2,
mode='average_inc_pad')
layer = batch_norm(
layers.Conv2DLayer(layer,
640,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
prely = batch_norm(
layers.Conv2DLayer(layer,
1024,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
featm = batch_norm(
layers.Conv2DLayer(prely,
640,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
feat_map = batch_norm(
layers.Conv2DLayer(featm,
100,
filter_size=(1, 1),
nonlinearity=rectify,
name="feat_map"))
maskm = batch_norm(
layers.Conv2DLayer(prely,
100,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
mask_rep = batch_norm(layers.Conv2DLayer(maskm,
1,
filter_size=(1, 1),
nonlinearity=None),
beta=None,
gamma=None)
mask_map = SoftThresPerc(mask_rep,
perc=97.0,
alpha=0.1,
beta=init.Constant(0.5),
tight=100.0,
name="mask_map")
enlyr = ChInnerProdMerge(feat_map, mask_map, name="encoder")
layer = batch_norm(
layers.Deconv2DLayer(enlyr,
1024,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
640,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
640,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
240,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
120,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = layers.Deconv2DLayer(layer,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
glblf = batch_norm(
layers.Conv2DLayer(prely,
128,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
glblf = layers.Pool2DLayer(glblf,
pool_size=(5, 5),
stride=5,
mode='average_inc_pad')
glblf = batch_norm(
layers.Conv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
gllyr = batch_norm(layers.Conv2DLayer(glblf,
5,
filter_size=(1, 1),
nonlinearity=rectify),
name="global_feature")
glblf = batch_norm(
layers.Deconv2DLayer(gllyr,
256,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(9, 9),
stride=5,
crop=(2, 2),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = layers.Deconv2DLayer(glblf,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
layer = layers.ElemwiseSumLayer([layer, glblf])
network = ReshapeLayer(layer, ([0], -1))
mask_map.beta.set_value(np.float32(0.9 * mask_map.beta.get_value()))
old_params = layers.get_all_params(network, trainable=True)
# Adding more layers
aug_var = T.matrix('aug_var')
target_var = T.imatrix('targets')
add_a = batch_norm(
layers.Conv2DLayer(enlyr,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_b = batch_norm(
layers.Conv2DLayer(add_a,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_c = batch_norm(
layers.Conv2DLayer(add_b,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_d = batch_norm(
layers.Conv2DLayer(add_c,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_0 = layers.Pool2DLayer(add_d,
pool_size=(25, 25),
stride=25,
mode='average_inc_pad')
add_1 = batch_norm(
layers.DenseLayer(add_0, 100, nonlinearity=leaky_rectify))
add_2 = batch_norm(
layers.DenseLayer(gllyr, 320, nonlinearity=leaky_rectify))
add_3 = batch_norm(
layers.DenseLayer(add_2, 320, nonlinearity=leaky_rectify))
add_4 = batch_norm(
layers.DenseLayer(add_3, 100, nonlinearity=leaky_rectify))
aug_layer = layers.InputLayer(shape=(None, aug_fea_n), input_var=aug_var)
cat_layer = lasagne.layers.ConcatLayer([add_1, add_4, aug_layer], axis=1)
hidden_layer = layers.DenseLayer(cat_layer, 80, nonlinearity=leaky_rectify)
network = layers.DenseLayer(hidden_layer, classn, nonlinearity=sigmoid)
all_params = layers.get_all_params(network, trainable=True)
new_params = [x for x in all_params if x not in old_params]
return network, new_params, input_var, aug_var, target_var
def build_autoencoder_network():
input_var = T.tensor4('input_var')
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var)
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
prely = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
featm = batch_norm(
layers.Conv2DLayer(prely,
180,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
feat_map = batch_norm(
layers.Conv2DLayer(featm,
120,
filter_size=(1, 1),
nonlinearity=rectify,
name="feat_map"))
maskm = batch_norm(
layers.Conv2DLayer(prely,
120,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
mask_rep = batch_norm(layers.Conv2DLayer(maskm,
1,
filter_size=(1, 1),
nonlinearity=None),
beta=None,
gamma=None)
mask_map = SoftThresPerc(mask_rep,
perc=99.9,
alpha=0.5,
beta=init.Constant(0.5),
tight=100.0,
name="mask_map")
layer = ChInnerProdMerge(feat_map, mask_map, name="encoder")
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = layers.Deconv2DLayer(layer,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
glblf = batch_norm(
layers.Conv2DLayer(prely,
100,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
glblf = layers.Pool2DLayer(glblf,
pool_size=(5, 5),
stride=5,
mode='average_inc_pad')
glblf = batch_norm(
layers.Conv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(layers.Conv2DLayer(glblf,
3,
filter_size=(1, 1),
nonlinearity=rectify),
name="global_feature")
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(9, 9),
stride=5,
crop=(2, 2),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
48,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
48,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
48,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = layers.Deconv2DLayer(glblf,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
layer = layers.ElemwiseSumLayer([layer, glblf])
network = ReshapeLayer(layer, ([0], -1))
layers.set_all_param_values(network,
pickle.load(open(filename_model_ae, 'rb')))
feat_var = lasagne.layers.get_output(feat_map, deterministic=True)
mask_var = lasagne.layers.get_output(mask_map, deterministic=True)
outp_var = lasagne.layers.get_output(network, deterministic=True)
return network, input_var, feat_var, mask_var, outp_var
def build_autoencoder_network():
input_var = T.tensor4('input_var')
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var)
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
120,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = layers.Pool2DLayer(layer,
pool_size=(2, 2),
stride=2,
mode='average_inc_pad')
layer = batch_norm(
layers.Conv2DLayer(layer,
240,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = layers.Pool2DLayer(layer,
pool_size=(2, 2),
stride=2,
mode='average_inc_pad')
layer = batch_norm(
layers.Conv2DLayer(layer,
640,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
prely = batch_norm(
layers.Conv2DLayer(layer,
1024,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
featm = batch_norm(
layers.Conv2DLayer(prely,
640,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
feat_map = batch_norm(
layers.Conv2DLayer(featm,
100,
filter_size=(1, 1),
nonlinearity=rectify,
name="feat_map"))
mask_map = SoftThresPerc(feat_map,
perc=98.4,
alpha=0.1,
beta=init.Constant(0.5),
tight=20.0,
name="mask_map")
layer = mask_map
layer = batch_norm(
layers.Deconv2DLayer(layer,
1024,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
640,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
640,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
240,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
120,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = layers.Deconv2DLayer(layer,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
glblf = batch_norm(
layers.Conv2DLayer(prely,
128,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
glblf = layers.Pool2DLayer(glblf,
pool_size=(5, 5),
stride=5,
mode='average_inc_pad')
glblf = batch_norm(
layers.Conv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(layers.Conv2DLayer(glblf,
5,
filter_size=(1, 1),
nonlinearity=rectify),
name="global_feature")
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
256,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(9, 9),
stride=5,
crop=(2, 2),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = layers.Deconv2DLayer(glblf,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
layer = layers.ElemwiseSumLayer([layer, glblf])
network = ReshapeLayer(layer, ([0], -1))
mask_var = lasagne.layers.get_output(mask_map)
output_var = lasagne.layers.get_output(network)
return network, input_var, mask_var, output_var
def build_autoencoder_network():
input_var = T.tensor4('input_var')
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var)
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
prely = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
featm = batch_norm(
layers.Conv2DLayer(prely,
180,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
feat_map = batch_norm(
layers.Conv2DLayer(featm,
120,
filter_size=(1, 1),
nonlinearity=rectify,
name="feat_map"))
maskm = batch_norm(
layers.Conv2DLayer(prely,
100,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
mask_rep = batch_norm(layers.Conv2DLayer(maskm,
1,
filter_size=(1, 1),
nonlinearity=None),
beta=None,
gamma=None)
mask_map = SoftThresPerc(mask_rep,
perc=90.0,
alpha=0.5,
beta=init.Constant(0.1),
tight=100.0,
name="mask_map")
layer = ChInnerProdMerge(feat_map, mask_map, name="encoder")
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
80,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = layers.Deconv2DLayer(layer,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
glblf = batch_norm(
layers.Conv2DLayer(prely,
100,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
glblf = layers.Pool2DLayer(glblf,
pool_size=(20, 20),
stride=20,
mode='average_inc_pad')
glblf = batch_norm(
layers.Conv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(layers.Conv2DLayer(glblf,
3,
filter_size=(1, 1),
nonlinearity=rectify),
name="global_feature")
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = layers.Upscale2DLayer(glblf, scale_factor=20)
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
48,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
48,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
48,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = layers.Deconv2DLayer(glblf,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
layer = layers.ElemwiseSumLayer([layer, glblf])
network = ReshapeLayer(layer, ([0], -1))
mask_var = lasagne.layers.get_output(mask_map)
output_var = lasagne.layers.get_output(network)
return network, input_var, mask_var, output_var
def build_network_from_ae(classn):
input_var = T.tensor4('input_var');
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = layers.Pool2DLayer(layer, pool_size=(2,2), stride=2, mode='average_inc_pad');
layer = batch_norm(layers.Conv2DLayer(layer, 240, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 320, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = layers.Pool2DLayer(layer, pool_size=(2,2), stride=2, mode='average_inc_pad');
layer = batch_norm(layers.Conv2DLayer(layer, 640, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
prely = batch_norm(layers.Conv2DLayer(layer, 1024, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
featm = batch_norm(layers.Conv2DLayer(prely, 640, filter_size=(1,1), nonlinearity=leaky_rectify));
feat_map = batch_norm(layers.Conv2DLayer(featm, 100, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
maskm = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
mask_rep = batch_norm(layers.Conv2DLayer(maskm, 1, filter_size=(1,1), nonlinearity=None), beta=None, gamma=None);
mask_map = SoftThresPerc(mask_rep, perc=0.0, alpha=0.1, beta=init.Constant(0.5), tight=100.0, bias=-10, name="mask_map");
enlyr = ChInnerProdMerge(feat_map, mask_map, name="encoder");
layer = batch_norm(layers.Deconv2DLayer(enlyr, 1024, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 640, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 640, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 240, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.Deconv2DLayer(layer, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
glblf = batch_norm(layers.Conv2DLayer(prely, 128, filter_size=(1,1), nonlinearity=leaky_rectify));
glblf = layers.Pool2DLayer(glblf, pool_size=(5,5), stride=5, mode='average_inc_pad');
glblf = batch_norm(layers.Conv2DLayer(glblf, 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
gllyr = batch_norm(layers.Conv2DLayer(glblf, 5, filter_size=(1,1), nonlinearity=rectify), name="global_feature");
glblf = batch_norm(layers.Deconv2DLayer(gllyr, 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(9,9), stride=5, crop=(2,2), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Deconv2DLayer(glblf, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
layer = layers.ElemwiseSumLayer([layer, glblf]);
network = ReshapeLayer(layer, ([0], -1));
layers.set_all_param_values(network, pickle.load(open(filename_model_ae, 'rb')));
mask_map.beta.set_value(np.float32(-10.0*mask_map.beta.get_value()));
# Adding more layers
aug_var = T.matrix('aug_var');
target_var = T.imatrix('targets');
add_a = layers.Conv2DLayer(enlyr, 320, filter_size=(1,1), nonlinearity=leaky_rectify);
add_b = layers.Conv2DLayer(add_a, 320, filter_size=(1,1), nonlinearity=leaky_rectify);
add_c = layers.Conv2DLayer(add_b, 320, filter_size=(1,1), nonlinearity=leaky_rectify);
add_d = layers.Conv2DLayer(add_c, 320, filter_size=(1,1), nonlinearity=leaky_rectify);
add_0 = layers.Pool2DLayer(add_d, pool_size=(15,15), stride=15, mode='average_inc_pad');
add_1 = layers.DenseLayer(add_0, 100, nonlinearity=leaky_rectify);
add_2 = layers.DenseLayer(gllyr, 320, nonlinearity=leaky_rectify);
add_3 = layers.DenseLayer(add_2, 320, nonlinearity=leaky_rectify);
add_4 = layers.DenseLayer(add_3, 100, nonlinearity=leaky_rectify);
aug_layer = layers.InputLayer(shape=(None, aug_fea_n), input_var=aug_var);
cat_layer = lasagne.layers.ConcatLayer([add_1, add_4, aug_layer], axis=1);
hidden_layer = layers.DenseLayer(cat_layer, 80, nonlinearity=leaky_rectify);
network = layers.DenseLayer(hidden_layer, classn, nonlinearity=sigmoid);
new_params = [add_a.W, add_a.b, add_b.W, add_b.b, add_c.W, add_c.b, add_d.W, add_d.b, add_1.W, add_1.b, add_2.W, add_2.b, add_3.W, add_3.b, add_4.W, add_4.b, hidden_layer.W, hidden_layer.b, network.W, network.b];
return network, new_params, input_var, aug_var, target_var;
def build_network_from_ae(classn):
input_var = T.tensor4('input_var');
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = layers.Pool2DLayer(layer, pool_size=(2,2), stride=2, mode='average_inc_pad');
layer = batch_norm(layers.Conv2DLayer(layer, 240, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 320, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = layers.Pool2DLayer(layer, pool_size=(2,2), stride=2, mode='average_inc_pad');
layer = batch_norm(layers.Conv2DLayer(layer, 640, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
prely = batch_norm(layers.Conv2DLayer(layer, 1024, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
featm = batch_norm(layers.Conv2DLayer(prely, 640, filter_size=(1,1), nonlinearity=leaky_rectify));
feat_map = batch_norm(layers.Conv2DLayer(featm, 100, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
maskm = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
mask_rep = batch_norm(layers.Conv2DLayer(maskm, 1, filter_size=(1,1), nonlinearity=None), beta=None, gamma=None);
mask_map = SoftThresPerc(mask_rep, perc=98.4, alpha=0.1, beta=init.Constant(0.5), tight=100.0, name="mask_map");
enlyr = ChInnerProdMerge(feat_map, mask_map, name="encoder");
layer = batch_norm(layers.Deconv2DLayer(enlyr, 1024, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 640, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 640, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 240, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.Deconv2DLayer(layer, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
glblf = batch_norm(layers.Conv2DLayer(prely, 128, filter_size=(1,1), nonlinearity=leaky_rectify));
glblf = layers.Pool2DLayer(glblf, pool_size=(5,5), stride=5, mode='average_inc_pad');
glblf = batch_norm(layers.Conv2DLayer(glblf, 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
gllyr = batch_norm(layers.Conv2DLayer(glblf, 5, filter_size=(1,1), nonlinearity=rectify), name="global_feature");
glblf = batch_norm(layers.Deconv2DLayer(gllyr, 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(9,9), stride=5, crop=(2,2), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Deconv2DLayer(glblf, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
layer = layers.ElemwiseSumLayer([layer, glblf]);
network = ReshapeLayer(layer, ([0], -1));
layers.set_all_param_values(network, pickle.load(open(filename_model_ae, 'rb')));
old_params = layers.get_all_params(network, trainable=True);
# Adding more layers
aug_var = T.matrix('aug_var');
target_var = T.imatrix('targets');
add_a = batch_norm(layers.Conv2DLayer(enlyr, 320, filter_size=(1,1), nonlinearity=leaky_rectify));
add_b = batch_norm(layers.Conv2DLayer(add_a, 320, filter_size=(1,1), nonlinearity=leaky_rectify));
add_c = batch_norm(layers.Conv2DLayer(add_b, 320, filter_size=(1,1), nonlinearity=leaky_rectify));
add_d = batch_norm(layers.Conv2DLayer(add_c, 320, filter_size=(1,1), nonlinearity=leaky_rectify));
add_0 = layers.Pool2DLayer(add_d, pool_size=(25,25), stride=25, mode='average_inc_pad');
add_1 = batch_norm(layers.DenseLayer(add_0, 100, nonlinearity=leaky_rectify));
add_2 = batch_norm(layers.DenseLayer(gllyr, 320, nonlinearity=leaky_rectify));
add_3 = batch_norm(layers.DenseLayer(add_2, 320, nonlinearity=leaky_rectify));
add_4 = batch_norm(layers.DenseLayer(add_3, 100, nonlinearity=leaky_rectify));
aug_layer = layers.InputLayer(shape=(None, aug_fea_n), input_var=aug_var);
cat_layer = lasagne.layers.ConcatLayer([add_1, add_4, aug_layer], axis=1);
hidden_layer = layers.DenseLayer(cat_layer, 80, nonlinearity=leaky_rectify);
network = layers.DenseLayer(hidden_layer, classn, nonlinearity=sigmoid);
layers.set_all_param_values(network, pickle.load(open('model_vals/deep_conv_classification_alt48_luad10_skcm10_lr0.py_e32_cv0.pkl', 'rb')));
all_params = layers.get_all_params(network, trainable=True);
new_params = [x for x in all_params if x not in old_params];
return network, new_params, input_var, aug_var, target_var;
