def build_model():
l_in = nn.layers.InputLayer((None, n_candidates_per_patient, 1,) + p_transform['patch_size'])
l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
l_target = nn.layers.InputLayer((batch_size,))
penultimate_layer = load_pretrained_model(l_in_rshp)
l = drop(penultimate_layer, name='drop_final')
l = dense(l, 128, name='dense_final')
l = nn.layers.DenseLayer(l, num_units=1, W=nn.init.Orthogonal(),
nonlinearity=None, name='dense_p_benign')
l = nn.layers.ReshapeLayer(l, (-1, n_candidates_per_patient, 1), name='reshape2patients')
l_out = nn_lung.AggAllBenignExp(l, name='aggregate_all_nodules_benign')
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
def build_model():
l_in = nn.layers.InputLayer((
None,
n_candidates_per_patient,
1,
) + p_transform['patch_size'])
l_in_rshp = nn.layers.ReshapeLayer(l_in, (
-1,
1,
) + p_transform['patch_size'])
l_target = nn.layers.InputLayer((batch_size, ))
l = dnn.Conv3DDNNLayer(l_in_rshp,
filter_size=3,
num_filters=64,
pad='valid',
W=nn.init.Orthogonal(),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = nn.layers.GlobalPoolLayer(l)
l = nn.layers.DenseLayer(l,
num_units=1,
W=nn.init.Orthogonal(),
nonlinearity=None)
l = nn.layers.ReshapeLayer(l, (-1, n_candidates_per_patient, 1))
l_out = nn_lung.AggAllBenignExp(l)
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out,
l_target)
def build_model():
l_in = nn.layers.InputLayer((
None,
n_candidates_per_patient,
1,
) + p_transform['patch_size'])
l_in_un = nn_lung.Unbroadcast(l_in)
l_in_rshp = nn.layers.ReshapeLayer(l_in_un, (
-1,
1,
) + p_transform['patch_size'])
l_target = nn.layers.InputLayer((batch_size, ))
l = conv3(l_in_rshp, num_filters=128)
l = conv3(l, num_filters=128)
l = max_pool(l)
l = conv3(l, num_filters=128)
l = conv3(l, num_filters=128)
l = max_pool(l)
l = conv3(l, num_filters=256)
l = conv3(l, num_filters=256)
l = conv3(l, num_filters=256)
l = dense_prelu_layer(l, num_units=512)
l = dense_prelu_layer(l, num_units=512)
l = nn.layers.DenseLayer(l,
num_units=1,
W=nn.init.Orthogonal(),
nonlinearity=None)
l = nn.layers.ReshapeLayer(l, (-1, n_candidates_per_patient, 1))
l_out = nn_lung.AggAllBenignExp(l)
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out,
l_target)
