def create_shallow_net(in_chans, input_time_length):
# receptive field size is determined by model architecture
n_classes = 2
# ensure reproducibility by resetting lasagne/theano random generator
lasagne.random.set_rng(RandomState(34734))
shallow_net = ShallowFBCSPNet(in_chans,
input_time_length,
n_classes,
n_filters_time=40,
filter_time_length=25,
n_filters_spat=40,
pool_time_length=75,
pool_time_stride=15,
final_dense_length=30,
conv_nonlin=square,
pool_mode='average_exc_pad',
pool_nonlin=safe_log,
split_first_layer=True,
batch_norm=True,
batch_norm_alpha=0.1,
drop_prob=0.5)
final_layer = shallow_net.get_layers()[-1]
final_layer = ClipLayer(final_layer, 1e-4, 1 - 1e-4)
return final_layer
def create_shallow_net(in_chans, input_time_length, final_dense_length,
n_classes):
shallow_net = create_shallow_model(in_chans, input_time_length,
final_dense_length, n_classes)
final_layer = shallow_net.get_layers()[-1]
final_layer = ClipLayer(final_layer, 1e-4, 1 - 1e-4)
return final_layer
def create_merged_net(in_chans,
input_time_length,
final_dense_length_deep,
final_dense_length_shallow,
n_classes,
filter_time_length_deep=13):
n_deep_features = 60
n_shallow_features = 40
batch_norm_before_merge = True
nonlin_before_merge = elu
deep_net = create_deep_model(in_chans,
input_time_length,
final_dense_length_deep,
n_classes,
filter_time_length=filter_time_length_deep)
shallow_net = create_shallow_model(in_chans, input_time_length,
final_dense_length_shallow, n_classes)
merged_net = MergedNet([
deep_net,
shallow_net,
], [n_deep_features, n_shallow_features],
n_classes,
batch_norm_before_merge=batch_norm_before_merge,
nonlin_before_merge=nonlin_before_merge)
final_layer = merged_net.get_layers()[-1]
final_layer = ClipLayer(final_layer, 1e-4, 1 - 1e-4)
return final_layer
def create_deep_net(in_chans, input_time_length):
num_filters_time = 25
filter_time_length = 10
num_filters_spat = 25
pool_time_length = 3
pool_time_stride = 3
num_filters_2 = 50
filter_length_2 = 10
num_filters_3 = 100
filter_length_3 = 10
num_filters_4 = 200
filter_length_4 = 10
final_dense_length = 2
n_classes = 4
final_nonlin = softmax
first_nonlin = elu
first_pool_mode = 'max'
first_pool_nonlin = identity
later_nonlin = elu
later_pool_mode = 'max'
later_pool_nonlin = identity
drop_in_prob = 0.0
drop_prob = 0.5
batch_norm_alpha = 0.1
double_time_convs = False
split_first_layer = True
batch_norm = True
d5net = Deep5Net(in_chans=in_chans,
input_time_length=input_time_length,
num_filters_time=num_filters_time,
filter_time_length=filter_time_length,
num_filters_spat=num_filters_spat,
pool_time_length=pool_time_length,
pool_time_stride=pool_time_stride,
num_filters_2=num_filters_2,
filter_length_2=filter_length_2,
num_filters_3=num_filters_3,
filter_length_3=filter_length_3,
num_filters_4=num_filters_4,
filter_length_4=filter_length_4,
final_dense_length=final_dense_length,
n_classes=n_classes,
final_nonlin=final_nonlin,
first_nonlin=first_nonlin,
first_pool_mode=first_pool_mode,
first_pool_nonlin=first_pool_nonlin,
later_nonlin=later_nonlin,
later_pool_mode=later_pool_mode,
later_pool_nonlin=later_pool_nonlin,
drop_in_prob=drop_in_prob,
drop_prob=drop_prob,
batch_norm_alpha=batch_norm_alpha,
double_time_convs=double_time_convs,
split_first_layer=split_first_layer,
batch_norm=batch_norm)
final_layer = d5net.get_layers()[-1]
final_layer = ClipLayer(final_layer, 1e-4, 1 - 1e-4)
return final_layer
def create_deep_net(in_chans,
input_time_length,
final_dense_length,
n_classes,
filter_time_length=10,
filter_length_4=10):
d5net = create_deep_model(in_chans,
input_time_length,
final_dense_length,
n_classes,
filter_time_length=filter_time_length,
filter_length_4=filter_length_4)
final_layer = d5net.get_layers()[-1]
final_layer = ClipLayer(final_layer, 1e-4, 1 - 1e-4)
return final_layer
def create_deep_net(in_chans, input_time_length):
# receptive field size is determined by model architecture
num_filters_time = 25
filter_time_length = 10
num_filters_spat = 25
pool_time_length = 3
pool_time_stride = 3
num_filters_2 = 50
filter_length_2 = 10
num_filters_3 = 100
filter_length_3 = 10
num_filters_4 = 200
filter_length_4 = 10
final_dense_length = 2
n_classes = 2
final_nonlin = softmax
first_nonlin = elu
first_pool_mode = 'max'
first_pool_nonlin = identity
later_nonlin = elu
later_pool_mode = 'max'
later_pool_nonlin = identity
drop_in_prob = 0.0
drop_prob = 0.5
batch_norm_alpha = 0.1
double_time_convs = False
split_first_layer = True
batch_norm = True
# ensure reproducibility by resetting lasagne/theano random generator
lasagne.random.set_rng(RandomState(34734))
d5net = Deep5Net(in_chans=in_chans,
input_time_length=input_time_length,
num_filters_time=num_filters_time,
filter_time_length=filter_time_length,
num_filters_spat=num_filters_spat,
pool_time_length=pool_time_length,
pool_time_stride=pool_time_stride,
num_filters_2=num_filters_2,
filter_length_2=filter_length_2,
num_filters_3=num_filters_3,
filter_length_3=filter_length_3,
num_filters_4=num_filters_4,
filter_length_4=filter_length_4,
final_dense_length=final_dense_length,
n_classes=n_classes,
final_nonlin=final_nonlin,
first_nonlin=first_nonlin,
first_pool_mode=first_pool_mode,
first_pool_nonlin=first_pool_nonlin,
later_nonlin=later_nonlin,
later_pool_mode=later_pool_mode,
later_pool_nonlin=later_pool_nonlin,
drop_in_prob=drop_in_prob,
drop_prob=drop_prob,
batch_norm_alpha=batch_norm_alpha,
double_time_convs=double_time_convs,
split_first_layer=split_first_layer,
batch_norm=batch_norm)
final_layer = d5net.get_layers()[-1]
final_layer = ClipLayer(final_layer, 1e-4, 1 - 1e-4)
return final_layer
def run_exp(i_fold):
# ensure reproducibility by resetting lasagne/theano random generator
lasagne.random.set_rng(RandomState(34734))
d5net = Deep5Net(in_chans=in_chans,
input_time_length=input_time_length,
num_filters_time=num_filters_time,
filter_time_length=filter_time_length,
num_filters_spat=num_filters_spat,
pool_time_length=pool_time_length,
pool_time_stride=pool_time_stride,
num_filters_2=num_filters_2,
filter_length_2=filter_length_2,
num_filters_3=num_filters_3,
filter_length_3=filter_length_3,
num_filters_4=num_filters_4,
filter_length_4=filter_length_4,
final_dense_length=final_dense_length,
n_classes=n_classes,
final_nonlin=final_nonlin,
first_nonlin=first_nonlin,
first_pool_mode=first_pool_mode,
first_pool_nonlin=first_pool_nonlin,
later_nonlin=later_nonlin,
later_pool_mode=later_pool_mode,
later_pool_nonlin=later_pool_nonlin,
drop_in_prob=drop_in_prob,
drop_prob=drop_prob,
batch_norm_alpha=batch_norm_alpha,
double_time_convs=double_time_convs,
split_first_layer=split_first_layer,
batch_norm=batch_norm)
final_layer = d5net.get_layers()[-1]
final_layer = ClipLayer(final_layer, 1e-4, 1 - 1e-4)
dataset_splitter = CntTrialSingleFoldSplitter(n_folds=10,
i_test_fold=i_fold,
shuffle=True)
iterator = CntWindowTrialIterator(
batch_size=45,
input_time_length=input_time_length,
n_sample_preds=get_n_sample_preds(final_layer))
monitors = [
LossMonitor(),
CntTrialMisclassMonitor(input_time_length=input_time_length),
KappaMonitor(input_time_length=iterator.input_time_length,
mode='max'),
RuntimeMonitor()
]
#n_no_decrease_max_epochs = 2
#n_max_epochs = 4
n_no_decrease_max_epochs = 80
n_max_epochs = 800
# real values for paper were 80 and 800
remember_best_chan = 'valid_' + stop_chan
stop_criterion = Or([
NoDecrease(remember_best_chan,
num_epochs=n_no_decrease_max_epochs),
MaxEpochs(num_epochs=n_max_epochs)
])
dataset = combined_set
splitter = dataset_splitter
updates_expression = adam
updates_modifier = MaxNormConstraintWithDefaults({})
preproc = None
exp = Experiment(final_layer,
dataset,
splitter,
preproc,
iterator,
loss_expression,
updates_expression,
updates_modifier,
monitors,
stop_criterion,
remember_best_chan,
run_after_early_stop,
batch_modifier=None)
if only_return_exp:
return exp
exp.setup()
exp.run()
return exp