def test_create_dataset():
train, val, test = prepare_train_test()
X, y = create_dataset(train[0:2])
_, accuracy = model.evaluate([testX, testX, testX],
testy,
batch_size=batch_size,
verbose=0)
return accuracy
if __name__ == '__main__':
# data_dir = Path(r'../data/physionet_sleep/eeg_fpz_cz').resolve()
data_dir = Path(r'../../data/physionet_sleep/eeg_fpz_cz').resolve()
train, val, test = prepare_train_test(data_dir=data_dir)
print(len(train))
from pysleep.data.data_generator import DataGenerator
X, y = create_dataset(train[0:3])
val_X, val_y = create_dataset(val[0:1])
# train_dl = DataGenerator(train[0:3])
# evaluate_model(X, y, val_X, val_y)
if 1:
# model = CNN1Head(model_name='CNN1Head_train3_test2', epochs=20, learning_rate=0.005, batch_size=32)
model = CNN3Head(model_name='CNN3Head_train10_test0',
epochs=25,
learning_rate=0.001,
batch_size=16,
metric='accuracy')
model.build_model()
# hist = model.fit_model(train_dl)
hist = model.fit_model(X, y, val_X,
val_y) # validation_data=(val_X,val_y))