def build_model(input_layer = None):
#################
# Regular model #
#################
input_size = data_sizes["sliced:data:singleslice"]
if input_layer:
l0 = input_layer
else:
l0 = nn.layers.InputLayer(input_size)
l1 = jonas_highway(l0, num_filters=64, num_conv=2, filter_size=(3,3), pool_size=(2,2))
l2 = jonas_highway(l1, num_filters=128, num_conv=2, filter_size=(3,3), pool_size=(2,2))
l3 = jonas_highway(l2, num_filters=256, num_conv=3, filter_size=(3,3), pool_size=(2,2))
l4 = jonas_highway(l3, num_filters=512, num_conv=3, filter_size=(3,3), pool_size=(2,2))
l5 = jonas_highway(l4, num_filters=512, num_conv=3, filter_size=(3,3), pool_size=(2,2))
# Systole Dense layers
ldsys1 = nn.layers.DenseLayer(l5, num_units=512, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
ldsys1drop = nn.layers.dropout(ldsys1, p=0.5)
ldsys2 = nn.layers.DenseLayer(ldsys1drop, num_units=512, W=nn.init.Orthogonal("relu"),b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
ldsys2drop = nn.layers.dropout(ldsys2, p=0.5)
ldsys3 = nn.layers.DenseLayer(ldsys2drop, num_units=600, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.softmax)
ldsys3drop = nn.layers.dropout(ldsys3, p=0.5) # dropout at the output might encourage adjacent neurons to correllate
ldsys3dropnorm = layers.NormalisationLayer(ldsys3drop)
l_systole = layers.CumSumLayer(ldsys3dropnorm)
# Diastole Dense layers
lddia1 = nn.layers.DenseLayer(l5, num_units=512, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
lddia1drop = nn.layers.dropout(lddia1, p=0.5)
lddia2 = nn.layers.DenseLayer(lddia1drop, num_units=512, W=nn.init.Orthogonal("relu"),b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
lddia2drop = nn.layers.dropout(lddia2, p=0.5)
lddia3 = nn.layers.DenseLayer(lddia2drop, num_units=600, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.softmax)
lddia3drop = nn.layers.dropout(lddia3, p=0.5) # dropout at the output might encourage adjacent neurons to correllate
lddia3dropnorm = layers.NormalisationLayer(lddia3drop)
l_diastole = layers.CumSumLayer(lddia3dropnorm)
return {
"inputs":{
"sliced:data:singleslice": l0
},
"outputs": {
"systole": l_systole,
"diastole": l_diastole,
},
"regularizable": {
ldsys1: l2_weight,
ldsys2: l2_weight,
ldsys3: l2_weight_out,
lddia1: l2_weight,
lddia2: l2_weight,
lddia3: l2_weight_out,
},
"meta_outputs": {
"systole": ldsys2,
"diastole": lddia2,
}
}
def build_model(input_layer=None):
#################
# Regular model #
#################
input_size = data_sizes["sliced:data:singleslice"]
if input_layer:
l0 = input_layer
else:
l0 = nn.layers.InputLayer(input_size)
l1 = jonas_highway(l0,
num_filters=64,
num_conv=2,
filter_size=(3, 3),
pool_size=(2, 2))
l2 = jonas_highway(l1,
num_filters=128,
num_conv=2,
filter_size=(3, 3),
pool_size=(2, 2))
l3 = jonas_highway(l2,
num_filters=256,
num_conv=3,
filter_size=(3, 3),
pool_size=(2, 2))
l4 = jonas_highway(l3,
num_filters=512,
num_conv=3,
filter_size=(3, 3),
pool_size=(2, 2))
l5 = jonas_highway(l4,
num_filters=512,
num_conv=3,
filter_size=(3, 3),
pool_size=(2, 2))
# Systole Dense layers
ldsys1 = nn.layers.DenseLayer(l5,
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
ldsys1drop = nn.layers.dropout(ldsys1, p=0.5)
ldsys2 = nn.layers.DenseLayer(ldsys1drop,
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
ldsys2drop = nn.layers.dropout(ldsys2, p=0.5)
ldsys3 = nn.layers.DenseLayer(ldsys2drop,
num_units=600,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.softmax)
ldsys3drop = nn.layers.dropout(
ldsys3, p=0.5
) # dropout at the output might encourage adjacent neurons to correllate
ldsys3dropnorm = layers.NormalisationLayer(ldsys3drop)
l_systole = layers.CumSumLayer(ldsys3dropnorm)
# Diastole Dense layers
lddia1 = nn.layers.DenseLayer(l5,
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
lddia1drop = nn.layers.dropout(lddia1, p=0.5)
lddia2 = nn.layers.DenseLayer(lddia1drop,
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
lddia2drop = nn.layers.dropout(lddia2, p=0.5)
lddia3 = nn.layers.DenseLayer(lddia2drop,
num_units=600,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.softmax)
lddia3drop = nn.layers.dropout(
lddia3, p=0.5
) # dropout at the output might encourage adjacent neurons to correllate
lddia3dropnorm = layers.NormalisationLayer(lddia3drop)
l_diastole = layers.CumSumLayer(lddia3dropnorm)
return {
"inputs": {
"sliced:data:singleslice": l0
},
"outputs": {
"systole": l_systole,
"diastole": l_diastole,
},
"regularizable": {
ldsys1: l2_weight,
ldsys2: l2_weight,
ldsys3: l2_weight_out,
lddia1: l2_weight,
lddia2: l2_weight,
lddia3: l2_weight_out,
},
"meta_outputs": {
"systole": ldsys2,
"diastole": lddia2,
}
}
def build_model():
#################
# Regular model #
#################
input_key = "sliced:data:singleslice:difference"
input_size = data_sizes[input_key]
l0 = InputLayer(input_size)
# add channel layer
# l0r = reshape(l0, (-1, 1, ) + input_size[1:])
# (batch, channel, time, x, y)
l0_n = batch_norm(l0)
l = jonas_highway(
l0_n,
num_filters=64,
filter_size=(3, 3),
axis=(2, 3),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l = batch_norm(l)
l = jonas_highway(
l,
num_filters=64,
filter_size=(3, 3),
axis=(2, 3),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l = batch_norm(l)
l = jonas_highway(
l,
num_filters=64,
filter_size=(3, 3),
num_conv=2,
axis=(2, 3),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l = batch_norm(l)
l_dense = lasagne.layers.DenseLayer(
lasagne.layers.DropoutLayer(l),
num_units=600,
nonlinearity=lasagne.nonlinearities.softmax)
l_systole = CumSumLayer(l_dense)
#===================================================================================
l = jonas_highway(
l0_n,
num_filters=64,
filter_size=(3, 3),
axis=(2, 3),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l = batch_norm(l)
l = jonas_highway(
l,
num_filters=64,
filter_size=(3, 3),
axis=(2, 3),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l = batch_norm(l)
l = jonas_highway(
l,
num_filters=64,
filter_size=(3, 3),
num_conv=2,
axis=(2, 3),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l = batch_norm(l)
l_dense = lasagne.layers.DenseLayer(
lasagne.layers.DropoutLayer(l),
num_units=600,
nonlinearity=lasagne.nonlinearities.softmax)
l_diastole = CumSumLayer(l_dense)
return {
"inputs": {
input_key: l0
},
"outputs": {
"systole": l_systole,
"diastole": l_diastole,
}
}
def build_model():
#################
# Regular model #
#################
input_key = "sliced:data:singleslice:difference"
input_size = data_sizes[input_key]
l0 = InputLayer(input_size)
# add channel layer
# l0r = reshape(l0, (-1, 1, ) + input_size[1:])
# (batch, channel, time, x, y)
l0_n = batch_norm(l0)
l = jonas_highway(l0_n, num_filters=64, filter_size=(3, 3),
axis=(2,3), channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l = batch_norm(l)
l = jonas_highway(l, num_filters=64, filter_size=(3, 3),
axis=(2,3), channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l = batch_norm(l)
l = jonas_highway(l, num_filters=64, filter_size=(3, 3),
num_conv=2,
axis=(2,3), channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l = batch_norm(l)
l_dense = lasagne.layers.DenseLayer(lasagne.layers.DropoutLayer(l),
num_units=600,
nonlinearity=lasagne.nonlinearities.softmax)
l_systole = CumSumLayer(l_dense)
#===================================================================================
l = jonas_highway(l0_n, num_filters=64, filter_size=(3, 3),
axis=(2,3), channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l = batch_norm(l)
l = jonas_highway(l, num_filters=64, filter_size=(3, 3),
axis=(2,3), channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l = batch_norm(l)
l = jonas_highway(l, num_filters=64, filter_size=(3, 3),
num_conv=2,
axis=(2,3), channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l = batch_norm(l)
l_dense = lasagne.layers.DenseLayer(lasagne.layers.DropoutLayer(l),
num_units=600,
nonlinearity=lasagne.nonlinearities.softmax)
l_diastole = CumSumLayer(l_dense)
return {
"inputs":{
input_key: l0
},
"outputs": {
"systole": l_systole,
"diastole": l_diastole,
}
}