def build_model():
#import here, such that our global variables are not overridden!
import j6_2ch_128mm, j6_4ch
meta_2ch = j6_2ch_128mm.build_model()
meta_4ch = j6_4ch.build_model()
l_age = nn.layers.InputLayer(data_sizes["sliced:meta:PatientAge"])
l_sex = nn.layers.InputLayer(data_sizes["sliced:meta:PatientSex"])
l_meta_2ch_systole = meta_2ch["meta_outputs"]["systole"]
l_meta_2ch_diastole = meta_2ch["meta_outputs"]["diastole"]
l_meta_4ch_systole = meta_4ch["meta_outputs"]["systole"]
l_meta_4ch_diastole = meta_4ch["meta_outputs"]["diastole"]
l_meta_systole = nn.layers.ConcatLayer([l_age, l_sex, l_meta_2ch_systole, l_meta_4ch_systole])
l_meta_diastole = nn.layers.ConcatLayer([l_age, l_sex, l_meta_2ch_diastole, l_meta_4ch_diastole])
ldsys1 = nn.layers.DenseLayer(l_meta_systole, 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)
lddia1 = nn.layers.DenseLayer(l_meta_diastole, 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)
submodels = [meta_2ch, meta_4ch]
return {
"inputs": dict({
"sliced:meta:PatientAge": l_age,
"sliced:meta:PatientSex": l_sex,
}, **{ k: v for d in [model["inputs"] for model in submodels]
for k, v in d.items() }
),
"outputs": {
"systole": l_systole,
"diastole": l_diastole,
},
"regularizable": dict({
}, **{ k: v for d in [model["regularizable"] for model in submodels if "regularizable" in model]
for k, v in d.items() }
),
"pretrained":{
j6_2ch_128mm.__name__: meta_2ch["outputs"],
j6_4ch.__name__: meta_4ch["outputs"],
},
"cutoff_gradients": [
] + [ v for d in [model["meta_outputs"] for model in submodels if "meta_outputs" in model]
for v in d.values() ]
}
def build_model():
#import here, such that our global variables are not overridden!
import j6_2ch_128mm, j6_4ch, je_ss_jonisc64small_360
sax_input = nn.layers.InputLayer(data_sizes["sliced:data:sax"])
sax_slices = nn.layers.ReshapeLayer(sax_input, (-1, [2], [3], [4]))
meta_sax = je_ss_jonisc64small_360.build_model(input_layer = sax_slices)
#reduce the number of parameters BEFORE reshaping! Keep 16 numbers per slice.
meta_sax_systole_reduced = nn.layers.DenseLayer(meta_sax["meta_outputs"]["systole"], num_units=16, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
meta_sax_diastole_reduced = nn.layers.DenseLayer(meta_sax["meta_outputs"]["diastole"], num_units=16, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
l_sax_systole = nn.layers.ReshapeLayer(meta_sax_systole_reduced, (-1, nr_slices, [1]))
l_sax_diastole = nn.layers.ReshapeLayer(meta_sax_diastole_reduced, (-1, nr_slices, [1]))
l_sax_systole_flat = nn.layers.DenseLayer(l_sax_systole, num_units=64, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
l_sax_diastole_flat = nn.layers.DenseLayer(l_sax_diastole, num_units=64, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
meta_2ch = j6_2ch_128mm.build_model()
meta_4ch = j6_4ch.build_model()
l_age = nn.layers.InputLayer(data_sizes["sliced:meta:PatientAge"])
l_sex = nn.layers.InputLayer(data_sizes["sliced:meta:PatientSex"])
l_locations = nn.layers.InputLayer(data_sizes["sliced:data:sax:locations"])
l_meta_2ch_systole = nn.layers.DenseLayer(meta_2ch["meta_outputs"]["systole"], num_units=64, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
l_meta_2ch_diastole = nn.layers.DenseLayer(meta_2ch["meta_outputs"]["diastole"], num_units=64, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
l_meta_4ch_systole = nn.layers.DenseLayer(meta_4ch["meta_outputs"]["systole"], num_units=64, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
l_meta_4ch_diastole = nn.layers.DenseLayer(meta_4ch["meta_outputs"]["diastole"], num_units=64, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
l_meta_systole = nn.layers.ConcatLayer([l_age, l_sex, l_meta_2ch_systole, l_meta_4ch_systole, l_locations, l_sax_systole_flat])
l_meta_diastole = nn.layers.ConcatLayer([l_age, l_sex, l_meta_2ch_diastole, l_meta_4ch_diastole, l_locations, l_sax_diastole_flat])
ldsys1 = nn.layers.DenseLayer(l_meta_systole, 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)
lddia1 = nn.layers.DenseLayer(l_meta_diastole, 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)
submodels = [meta_2ch, meta_4ch, meta_sax]
return {
"inputs": dict({
"sliced:data:sax": sax_input,
"sliced:meta:PatientAge": l_age,
"sliced:meta:PatientSex": l_sex,
"sliced:data:sax:locations": l_locations,
}, **{ k: v for d in [model["inputs"] for model in [meta_2ch, meta_4ch]]
for k, v in d.items() }
),
"outputs": {
"systole": l_systole,
"diastole": l_diastole,
},
"regularizable": dict({
}, **{ k: v for d in [model["regularizable"] for model in submodels if "regularizable" in model]
for k, v in d.items() }
),
"pretrained":{
j6_2ch_128mm.__name__: meta_2ch["outputs"],
j6_4ch.__name__: meta_4ch["outputs"],
je_ss_jonisc64small_360.__name__: meta_sax["outputs"],
},
"cutoff_gradients": [
] + [ v for d in [model["meta_outputs"] for model in submodels if "meta_outputs" in model]
for v in d.values() ]
}
