def main():
model = EEGNet(nb_classes=2,
Chans=25,
Samples=176,
dropoutRate=0.5,
kernLength=32,
F1=8,
D=2,
F2=16,
dropoutType='Dropout')
model.load_weights(f'BCICIV2a/weights/all.h5')
model._make_predict_function()
stream(model)
for i in range(0, FOLDS):
comp_trainX, comp_valX, comp_testX = add_kernel_dim(get_fold(
_compX, FOLDS, i, test_val_rest_split),
kernels=KERNELS)
comp_trainY, comp_valY, comp_testY = onehot(
get_fold(_compY, FOLDS, i, test_val_rest_split))
# weight file path
weight_file = f"{WEIGHT_PATH}/{i+1}.h5"
# initialise model
model = EEGNet(nb_classes=CLASSES,
Chans=chans,
Samples=samples,
dropoutRate=0.5,
kernLength=64,
F1=8,
D=2,
F2=16,
dropoutType='Dropout')
# compile model
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# train model and save weights
# train(model, {"x": comp_trainX, "y": comp_trainY}, {"x": comp_valX, "y": comp_valY}, weight_file, epochs=EPOCHS)
# load weights from file
model.load_weights(weight_file)
def get_model(transfer_paths=None):
K.clear_session()
model = EEGNet(nb_classes=3,
Chans=9,
Samples=250,
dropoutRate=0.5,
kernLength=64,
F1=8,
D=2,
F2=16,
dropoutType='Dropout')
# compile model loss function and optimizer for transfer learning
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# load base model
model.load_weights(BASE_WEIGHTS)
# K.clear_session()
print(transfer_paths)
if not transfer_paths or len(transfer_paths) is 0:
return (model, True)
print(len(transfer_paths))
try:
transfer_raw = read_raw_brainvision(transfer_paths[0], preload=True)
if len(transfer_paths) > 1:
for i in range(1, len(transfer_paths)):
print(i)
transfer_raw = concatenate_raws([
transfer_raw,
read_raw_brainvision(transfer_paths[i], preload=True)
])
except Exception as e:
print('failed', e)
return (model, None)
transX, transY = epoch_pilot(transfer_raw,
n_classes=3,
good_channels=GOODS,
resample=RESAMPLE,
trange=T_RANGE,
l_freq=LO_FREQ,
h_freq=HI_FREQ)
# separate 4:1 train:validation
transX, transY = stratify(transX, transY, 5)
trans_trainX, trans_valX = add_kernel_dim(get_fold(transX, 5, 0,
test_rest_split),
kernels=1)
trans_trainY, trans_valY = onehot(get_fold(transY, 5, 0, test_rest_split))
trans_valY, _ = onehot((trans_valY, []))
# perform transfer learning on the base model and selected transfer file
train(model, {
"x": trans_trainX,
"y": trans_trainY
}, {
"x": trans_valX,
"y": trans_valY
},
epochs=EPOCHS)
return (model, False)
# # compile model
# model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# # train model and save weights
# train(model, {"x": comp_trainX, "y": comp_trainY}, {"x": comp_valX, "y": comp_valY}, weight_file, epochs=EPOCHS)
models = []
for i in range(0, MODELS):
weight_file = f"{WEIGHT_PATH}/{i+1}.h5"
models.append(
EEGNet(nb_classes=CLASSES,
Chans=chans,
Samples=samples,
dropoutRate=0.5,
kernLength=64,
F1=8,
D=2,
F2=16,
dropoutType='Dropout'))
models[i].compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
models[i].load_weights(weight_file)
# stratify and split pilot data for transfer learning
skf = StratifiedKFold(PILOT_FOLDS, shuffle=True)
pilot_avg = {'acc': 0, 'bal': 0, 'kap': 0}
for i, (train_index, test_index) in enumerate(skf.split(_pilotX, _pilotY)):
# reset weights