def main():
    'Run this experiment'
    training_ints = initialize_instances('m_trg.csv')
    testing_ints = initialize_instances('m_test.csv')
    validation_ints = initialize_instances('m_val.csv')
    factory = BackPropagationNetworkFactory()
    measure = SumOfSquaresError()
    data_set = DataSet(training_ints)
    relu = RELU()
    rule = RPROPUpdateRule()
    oa_names = ['Backprop']
    classification_network = factory.createClassificationNetwork([INPUT_LAYER, HIDDEN_LAYER1, HIDDEN_LAYER2, HIDDEN_LAYER3, OUTPUT_LAYER], relu)
    train(BatchBackPropagationTrainer(data_set, classification_network, measure, rule), classification_network, 'Backprop', training_ints, validation_ints, testing_ints, measure)
Run this experiment
def main():
    
    training_ints = initialize_instances('m_trg.csv')
    testing_ints = initialize_instances('m_test.csv')
    validation_ints = initialize_instances('m_val.csv')
    factory = BackPropagationNetworkFactory()
    measure = SumOfSquaresError()
    data_set = DataSet(training_ints)
    relu = RELU()
    rule = RPROPUpdateRule()
    oa_names = ['Backprop']
    classification_network = factory.createClassificationNetwork([INPUT_LAYER, HIDDEN_LAYER1, HIDDEN_LAYER2, HIDDEN_LAYER3, OUTPUT_LAYER], relu)
    train(BatchBackPropagationTrainer(data_set, classification_network, measure, rule), classification_network, 'Backprop', training_ints, validation_ints, testing_ints, measure)