def main_predict(config):
predictions = []
for net_name in config['net_names']:
preds = np.load(config[net_name]['predictions_path'],
allow_pickle=True)
predictions.append(preds)
mean_ensemble = np.mean(np.array(predictions), axis=0)
np.save(file=config['final_predictions_path'], arr=mean_ensemble)
print('saved final mean ensemble predictions at path: {}'.format(
config['final_predictions_path']))
dummy_model = NNet(data_paths=config['data_paths'])
dummy_model.test_images_predictions = mean_ensemble
mean_sub = dummy_model.create_submission_file(
path=config['submission_path'], treshold=config['treshold'])
save_predictions_pdf(dummy_model, config)
def main_train(config):
for net_to_train, net_name in zip(config['net_types'],
config['net_names']):
net = NNet(net_type=net_to_train, data_paths=config['data_paths'])
single_model_training(
model=net,
save_path=config[net_name]['checkpoint'],
additional_epochs=config[net_name]['additional_epochs'],
competition_epochs=config[net_name]['competition_epochs'],
b_size=config[net_name]['batch_size'],
loss=config['loss'],
l_rate_a=config['learning_rate_additional_data'],
l_rate_b=config['learning_rate_competition_data'],
v=config['verbose'])
predictions = net.predict_test_data()
np.save(file=config[net_name]['predictions_path'], arr=predictions)
print('saved model predictions at path: {}'.format(
config[net_name]['predictions_path']))
sub = net.create_submission_file(path=config[net_name]['csv_path'],
treshold=config['treshold'])
print('saved model csv at path: {}'.format(
config[net_name]['csv_path']))