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()
