def build_model():
l_in = nn.layers.InputLayer((
None,
n_candidates_per_patient,
) + 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 = nn.layers.DenseLayer(penultimate_layer,
num_units=1,
W=nn.init.Orthogonal(),
nonlinearity=nn.nonlinearities.sigmoid,
name='dense_p_benign')
l = nn.layers.ReshapeLayer(l, (-1, n_candidates_per_patient, 1),
name='reshape2patients')
l_out = nn_lung.LogMeanExp(l, r=8, axis=(1, 2), name='LME')
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,
) + 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 = load_pretrained_model(l_in_rshp)
#ins = penultimate_layer.output_shape[1]
# l = conv3d(penultimate_layer, ins, filter_size=3, stride=2)
# #l = feat_red(l)
#
#
# l = nn.layers.DropoutLayer(l)
# #
# l = nn.layers.DenseLayer(l, num_units=256, W=nn.init.Orthogonal(),
# nonlinearity=nn.nonlinearities.rectify)
#l = nn.layers.DropoutLayer(l)
l = nn.layers.ReshapeLayer(l, (-1, n_candidates_per_patient, 1))
l_out = nn_lung.LogMeanExp(l, r=16, axis=(1, 2), name='LME')
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out,
l_target)
def build_model():
l_in = nn.layers.InputLayer((batch_size, n_candidates_per_patient, ) + 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,))
print l_in_rshp.output_shape
feat_layer = load_pretrained_model(l_in_rshp)
l = nn.layers.ReshapeLayer(feat_layer, (-1, n_candidates_per_patient), name='reshape2patients')
l_out = nn_lung.LogMeanExp(l, r=8, axis=(1,), name='LME')
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,))
penultimate_layer = load_pretrained_model(l_in_rshp)
l = drop(penultimate_layer, name='drop_final')
l = dense(l, 512, 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.LogMeanExp(l, name='aggregate_all_nodules_benign')
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
