def build_model():
l_in = nn.layers.InputLayer((None, nslices, 30) + patch_size)
l_in_slice_mask = nn.layers.InputLayer((None, nslices))
l_in_slice_location = nn.layers.InputLayer((None, nslices, 1))
l_in_sex_age = nn.layers.InputLayer((None, 2))
l_ins = [l_in, l_in_slice_mask, l_in_slice_location, l_in_sex_age]
l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 30) + patch_size) # (batch_size*nslices, 30, h,w)
submodel = subconfig().build_model(l_in_rshp)
# ------------------ systole
l_mu0 = submodel.mu_layers[0]
l_sigma0 = submodel.sigma_layers[0]
l_mu0 = nn.layers.ReshapeLayer(l_mu0, (-1, nslices, [1]))
l_sigma0 = nn.layers.ReshapeLayer(l_sigma0, (-1, nslices, [1]))
l_volume_mu_sigma0 = nn_heart.JeroenLayer([nn.layers.flatten(l_mu0), nn.layers.flatten(l_sigma0),
l_in_slice_mask, nn.layers.flatten(l_in_slice_location)],
trainable_scale=False)
l_volume_mu0 = nn.layers.SliceLayer(l_volume_mu_sigma0, indices=0, axis=-1)
l_volume_sigma0 = nn.layers.SliceLayer(l_volume_mu_sigma0, indices=1, axis=-1)
l_cdf0 = nn_heart.NormalCDFLayer(nn.layers.reshape(l_volume_mu0, ([0], 1)),
nn.layers.reshape(l_volume_sigma0, ([0], 1)))
# ------------------ diastole
l_mu1 = submodel.mu_layers[1]
l_sigma1 = submodel.sigma_layers[1]
l_mu1 = nn.layers.ReshapeLayer(l_mu1, (-1, nslices, [1]))
l_sigma1 = nn.layers.ReshapeLayer(l_sigma1, (-1, nslices, [1]))
l_volume_mu_sigma1 = nn_heart.JeroenLayer([nn.layers.flatten(l_mu1), nn.layers.flatten(l_sigma1),
l_in_slice_mask, nn.layers.flatten(l_in_slice_location)],
trainable_scale=False)
l_volume_mu1 = nn.layers.SliceLayer(l_volume_mu_sigma1, indices=0, axis=-1)
l_volume_sigma1 = nn.layers.SliceLayer(l_volume_mu_sigma1, indices=1, axis=-1)
l_cdf1 = nn_heart.NormalCDFLayer(nn.layers.reshape(l_volume_mu1, ([0], 1)),
nn.layers.reshape(l_volume_sigma1, ([0], 1)))
l_outs = [l_cdf0, l_cdf1]
l_top = nn.layers.MergeLayer(l_outs)
l_target_mu0 = nn.layers.InputLayer((None, 1))
l_target_mu1 = nn.layers.InputLayer((None, 1))
l_targets = [l_target_mu0, l_target_mu1]
train_params = nn.layers.get_all_params(l_top)
test_layes = [l_volume_mu_sigma0, l_volume_mu_sigma1]
return namedtuple('Model', ['l_ins', 'l_outs', 'l_targets', 'l_top', 'train_params', 'submodel', 'test_layers'])(
l_ins, l_outs,
l_targets,
l_top,
train_params,
submodel, test_layes)
def build_model(l_in=None):
l_in = nn.layers.InputLayer((None, 30) + patch_size) if not l_in else l_in
l = conv3(l_in, 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 = max_pool(l)
l = conv3(l, num_filters=512)
l = conv3(l, num_filters=512)
l = conv3(l, num_filters=512)
l = max_pool(l)
l = conv3(l, num_filters=512)
l = conv3(l, num_filters=512)
l = conv3(l, num_filters=512)
l = max_pool(l)
l_w = nn.layers.DenseLayer(nn.layers.dropout(l), num_units=1024, W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1))
l_d01 = nn.layers.DenseLayer(l, num_units=1024, W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1))
l_d02 = nn.layers.DenseLayer(nn.layers.dropout(l_d01), num_units=1024, W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1))
l_d02 = nn_heart.MultLayer(l_d02, l_w)
mu0 = nn.layers.DenseLayer(nn.layers.dropout(l_d02), num_units=1, W=nn.init.Orthogonal(),
b=nn.init.Constant(50), nonlinearity=nn_heart.lb_softplus())
sigma0 = nn.layers.DenseLayer(nn.layers.dropout(l_d02), num_units=1, W=nn.init.Orthogonal(),
b=nn.init.Constant(10), nonlinearity=nn_heart.lb_softplus())
l_cdf0 = nn_heart.NormalCDFLayer(mu0, sigma0, sigma_logscale=False, mu_logscale=False)
# ---------------------------------------------------------------
l_d11 = nn.layers.DenseLayer(l, num_units=1024, W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1))
l_d12 = nn.layers.DenseLayer(nn.layers.dropout(l_d11), num_units=1024, W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1))
l_d12 = nn_heart.MultLayer(l_d12, l_w)
mu1 = nn.layers.DenseLayer(nn.layers.dropout(l_d12), num_units=1, W=nn.init.Orthogonal(),
b=nn.init.Constant(100), nonlinearity=nn_heart.lb_softplus())
sigma1 = nn.layers.DenseLayer(nn.layers.dropout(l_d12), num_units=1, W=nn.init.Orthogonal(),
b=nn.init.Constant(10), nonlinearity=nn_heart.lb_softplus())
l_cdf1 = nn_heart.NormalCDFLayer(mu1, sigma1, sigma_logscale=False, mu_logscale=False)
l_outs = [l_cdf0, l_cdf1]
l_top = nn.layers.MergeLayer(l_outs)
l_target_mu0 = nn.layers.InputLayer((None, 1))
l_target_mu1 = nn.layers.InputLayer((None, 1))
l_targets = [l_target_mu0, l_target_mu1]
dense_layers = [l_w, l_d01, l_d02, l_d11, l_d12, mu0, sigma0, mu0, mu1]
mu_layers = [mu0, mu1]
sigma_layers = [sigma0, sigma1]
return namedtuple('Model', ['l_ins', 'l_outs', 'l_targets', 'l_top', 'dense_layers', 'mu_layers', 'sigma_layers'])(
[l_in], l_outs, l_targets,
l_top, dense_layers, mu_layers, sigma_layers)
def build_model(l_in=None):
l_in = nn.layers.InputLayer((None, 30) + patch_size) if not l_in else l_in
l = conv3(l_in, num_filters=64)
l = conv3(l, num_filters=64)
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 = max_pool(l)
l = conv3(l, num_filters=512)
l = conv3(l, num_filters=512)
l = conv3(l, num_filters=512)
l = max_pool(l)
l = conv3(l, num_filters=512)
l = conv3(l, num_filters=512)
l = conv3(l, num_filters=512)
l = max_pool(l)
l_d01 = nn.layers.DenseLayer(
l,
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
l_d02 = nn.layers.DenseLayer(
nn.layers.dropout(l_d01, p=0.5),
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
mu0 = nn.layers.DenseLayer(nn.layers.dropout(l_d02, p=0.5),
num_units=1,
W=nn.init.Orthogonal(),
b=nn.init.Constant(100),
nonlinearity=nn_heart.lb_softplus())
sigma0 = nn.layers.DenseLayer(nn.layers.dropout(l_d02, p=0.5),
num_units=1,
W=nn.init.Orthogonal(),
b=nn.init.Constant(20),
nonlinearity=nn_heart.lb_softplus())
l_cdf0 = nn_heart.NormalCDFLayer(mu0, sigma0)
# ---------------------------------------------------------------
l_d11 = nn.layers.DenseLayer(
l,
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
l_d12 = nn.layers.DenseLayer(
nn.layers.dropout(l_d11, p=0.5),
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
mu1 = nn.layers.DenseLayer(nn.layers.dropout(l_d12, p=0.5),
num_units=1,
W=nn.init.Orthogonal(),
b=nn.init.Constant(150),
nonlinearity=nn_heart.lb_softplus())
sigma1 = nn.layers.DenseLayer(nn.layers.dropout(l_d12, p=0.5),
num_units=1,
W=nn.init.Orthogonal(),
b=nn.init.Constant(20),
nonlinearity=nn_heart.lb_softplus())
l_cdf1 = nn_heart.NormalCDFLayer(mu1, sigma1)
l_outs = [l_cdf0, l_cdf1]
l_top = nn.layers.MergeLayer(l_outs)
l_target_mu0 = nn.layers.InputLayer((None, 1))
l_target_mu1 = nn.layers.InputLayer((None, 1))
l_targets = [l_target_mu0, l_target_mu1]
mu_layers = [mu0, mu1]
sigma_layers = [sigma0, sigma1]
return namedtuple(
'Model',
['l_ins', 'l_outs', 'l_targets', 'l_top', 'mu_layers', 'sigma_layers'
])([l_in], l_outs, l_targets, l_top, mu_layers, sigma_layers)
def build_model(l_in=None):
stage_depths = [2, 4, 8, 10, 4]
stage_sizes = [64, 96, 128, 192, 256]
l_in = nn.layers.InputLayer((None, 30) + patch_size) if not l_in else l_in
l = l_in
for i in xrange(len(stage_sizes)):
# First layer to change dimensions
l = conv3(l, num_filters=stage_sizes[i])
for _ in range(stage_depths[i] - 1):
l = nn_heart.highway_conv3(l)
l = max_pool(l)
l_d01 = nn.layers.DenseLayer(
l,
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
l_d02 = nn.layers.DenseLayer(
nn.layers.dropout(l_d01),
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
mu0 = nn.layers.DenseLayer(nn.layers.dropout(l_d02),
num_units=1,
W=nn.init.Orthogonal(),
b=nn.init.Constant(50),
nonlinearity=nn_heart.lb_softplus())
sigma0 = nn.layers.DenseLayer(nn.layers.dropout(l_d02),
num_units=1,
W=nn.init.Orthogonal(),
b=nn.init.Constant(10),
nonlinearity=nn_heart.lb_softplus())
l_cdf0 = nn_heart.NormalCDFLayer(mu0,
sigma0,
sigma_logscale=False,
mu_logscale=False)
# ---------------------------------------------------------------
l_d11 = nn.layers.DenseLayer(
l,
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
l_d12 = nn.layers.DenseLayer(
nn.layers.dropout(l_d11),
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
mu1 = nn.layers.DenseLayer(nn.layers.dropout(l_d12),
num_units=1,
W=nn.init.Orthogonal(),
b=nn.init.Constant(100),
nonlinearity=nn_heart.lb_softplus())
sigma1 = nn.layers.DenseLayer(nn.layers.dropout(l_d12),
num_units=1,
W=nn.init.Orthogonal(),
b=nn.init.Constant(10),
nonlinearity=nn_heart.lb_softplus())
l_cdf1 = nn_heart.NormalCDFLayer(mu1,
sigma1,
sigma_logscale=False,
mu_logscale=False)
l_outs = [l_cdf0, l_cdf1]
l_top = nn.layers.MergeLayer(l_outs)
l_target_mu0 = nn.layers.InputLayer((None, 1))
l_target_mu1 = nn.layers.InputLayer((None, 1))
l_targets = [l_target_mu0, l_target_mu1]
dense_layers = [l_d01, l_d02, l_d11, l_d12, mu0, sigma0, mu0, mu1]
mu_layers = [mu0, mu1]
sigma_layers = [sigma0, sigma1]
return namedtuple('Model', [
'l_ins', 'l_outs', 'l_targets', 'l_top', 'dense_layers', 'mu_layers',
'sigma_layers'
])([l_in], l_outs, l_targets, l_top, dense_layers, mu_layers, sigma_layers)
