def online_eval(): # evaluation code for online test handler = DataHandler() data = handler.generate_data(TRAIN_FILENAME) testing_data = handler.generate_data(TEST_FILENAME, "test") ann = ANN(9, 10, 1) for i in range(80): print(i + 1) ann.train(data, 5000) result = ann.test_without_true_label(testing_data, 0.23) handler.write_to_result(TEST_FILENAME, result)
def plot_iter(result): width = 10 x = np.arange(10, 101, 10) plt.ylim(0.6, 0.68) plt.ylabel("Precision") plt.xlabel("Iteration") plt.bar(x, [val for val in result], width, color="#ababab") plt.show() # plot node number in hidden layer figure def plot_node(result): width = 0.5 x = np.arange(1, 11, 1) plt.ylim(0.62, 0.66) plt.ylabel("Precision") plt.xlabel("Hidden Layer Node Number") plt.bar(x, [val for val in result], width, color="#ababab") plt.show() if __name__ == "__main__": handler = DataHandler() data = handler.generate_data(TRAIN_FILENAME) iteration_test(data) node_test(data) layer_test(data) cross_validation(5, 500, data) online_eval()