def build_model():
#################
# Regular model #
#################
l0 = InputLayer(data_sizes["sliced:data:ax"])
l0r = reshape(l0, (-1, 1, ) + data_sizes["sliced:data:ax"][1:])
# (batch, channel, time, axis, x, y)
# convolve over time
l1 = ConvolutionOverAxisLayer(l0r, num_filters=2, filter_size=(5,), axis=(2,), channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l1m = MaxPoolOverAxisLayer(l1, pool_size=(2,), axis=(2,))
# convolve over x and y
l2a = ConvolutionOver2DAxisLayer(l1m, num_filters=32, filter_size=(5, 5), stride=(2,2),
axis=(4,5), channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l2b = ConvolutionOver2DAxisLayer(l2a, num_filters=32, filter_size=(5, 5),
axis=(4,5), channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l2m = MaxPoolOver2DAxisLayer(l2b, pool_size=(3, 3), axis=(4,5))
# convolve over x, y and axis
l3a = ConvolutionOver3DAxisLayer(l2m, num_filters=32, filter_size=(3, 3, 3),
axis=(3,4,5), channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l3b = ConvolutionOver3DAxisLayer(l3a, num_filters=32, filter_size=(3, 3, 3),
axis=(3,4,5), channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l3m = MaxPoolOver3DAxisLayer(l3b, pool_size=(2, 2, 2), axis=(3,4,5))
# convolve over time
l4 = ConvolutionOverAxisLayer(l3m, num_filters=32, filter_size=(5,), axis=(2,), channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l4m = MaxPoolOverAxisLayer(l4, pool_size=(2,), axis=(2,))
# convolve over axis
l5 = ConvolutionOverAxisLayer(l4m, num_filters=32, filter_size=(3,), axis=(3,), channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
"""
l_d3 = lasagne.layers.DenseLayer(l3m,
num_units=2,
nonlinearity=lasagne.nonlinearities.identity)
l_systole = MuLogSigmaErfLayer(l_d3)
l_d3b = lasagne.layers.DenseLayer(l3m,
num_units=2,
nonlinearity=lasagne.nonlinearities.identity)
l_diastole = MuLogSigmaErfLayer(l_d3b)
"""
l_d3 = lasagne.layers.DenseLayer(l5,
num_units=600,
nonlinearity=lasagne.nonlinearities.softmax)
l_systole = CumSumLayer(l_d3)
l_d3b = lasagne.layers.DenseLayer(l5,
num_units=600,
nonlinearity=lasagne.nonlinearities.softmax)
l_diastole = CumSumLayer(l_d3b)
return {
"inputs":{
"sliced:data:ax": l0
},
"outputs":{
"systole": l_systole,
"diastole": l_diastole
}
}
def build_model():
#################
# Regular model #
#################
l0 = InputLayer(data_sizes["sliced:data:ax"])
l0r = reshape(l0, (
-1,
1,
) + data_sizes["sliced:data:ax"][1:])
# (batch, channel, time, axis, x, y)
# convolve over time
l1 = ConvolutionOverAxisLayer(
l0r,
num_filters=4,
filter_size=(3, ),
axis=(2, ),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l1b = ConvolutionOverAxisLayer(
l1,
num_filters=4,
filter_size=(3, ),
axis=(2, ),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l1m = MaxPoolOverAxisLayer(l1b, pool_size=(2, ), axis=(2, ))
# convolve over x and y
l2a = ConvolutionOver2DAxisLayer(
l1m,
num_filters=32,
filter_size=(3, 3),
axis=(4, 5),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l2b = ConvolutionOver2DAxisLayer(
l2a,
num_filters=32,
filter_size=(3, 3),
axis=(4, 5),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l2m = MaxPoolOver2DAxisLayer(l2b, pool_size=(2, 2), axis=(4, 5))
# convolve over x, y and axis
l3a = ConvolutionOver3DAxisLayer(
l2m,
num_filters=64,
filter_size=(3, 3, 3),
axis=(3, 4, 5),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l3b = ConvolutionOver3DAxisLayer(
l3a,
num_filters=64,
filter_size=(3, 3, 3),
axis=(3, 4, 5),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l3c = ConvolutionOver3DAxisLayer(
l3b,
num_filters=64,
filter_size=(3, 3, 3),
axis=(3, 4, 5),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l3m = MaxPoolOver3DAxisLayer(l3c, pool_size=(2, 2, 2), axis=(3, 4, 5))
# convolve over time
l4 = ConvolutionOverAxisLayer(
l3m,
num_filters=64,
filter_size=(3, ),
axis=(2, ),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l4m = MaxPoolOverAxisLayer(l4, pool_size=(2, ), axis=(2, ))
# convolve over axis
l5 = ConvolutionOverAxisLayer(
l4m,
num_filters=128,
filter_size=(3, ),
axis=(3, ),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
# convolve over x and y
l6a = ConvolutionOver2DAxisLayer(
l5,
num_filters=128,
filter_size=(3, 3),
axis=(4, 5),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l6b = ConvolutionOver2DAxisLayer(
l6a,
num_filters=128,
filter_size=(3, 3),
axis=(4, 5),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l6m = MaxPoolOver2DAxisLayer(l6b, pool_size=(2, 2), axis=(4, 5))
# convolve over time and x,y
l7 = ConvolutionOver3DAxisLayer(
l6m,
num_filters=32,
filter_size=(3, 3, 3),
axis=(2, 4, 5),
channel=1,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
)
l8 = lasagne.layers.DropoutLayer(l7)
l_d3a = lasagne.layers.DenseLayer(l8, num_units=600)
l_d3b = lasagne.layers.DropoutLayer(l_d3a)
l_systole = lasagne.layers.DenseLayer(
l_d3b, num_units=600, nonlinearity=lasagne.nonlinearities.softmax)
l_d3c = lasagne.layers.DenseLayer(l8, num_units=600)
l_d3d = lasagne.layers.DropoutLayer(l_d3c)
l_diastole = lasagne.layers.DenseLayer(
l_d3d, num_units=600, nonlinearity=lasagne.nonlinearities.softmax)
return {
"inputs": {
"sliced:data:ax": l0
},
"outputs": {
"systole:onehot": l_systole,
"diastole:onehot": l_diastole,
},
"regularizable": {
l_d3a: 0.25,
l_d3c: 0.25,
l_systole: 0.25,
l_diastole: 0.25,
}
}