Пример #1
0
def show_solution(P, T, path):
    best = load_network(path)
    Y = best.sim(P)
    area = plotroc(Y, T)
    print("")
    print("Stats for cut = 0.5")
    [num_correct_first, num_correct_second, total_performance, num_first, num_second, missed] = stat(Y, T)
    plt.title(path + "\nArea = " + str(area))
    plt.show()
Пример #2
0
# net = build_feedforward(8, 8, 1)

epochs = 10

# best = benchmark(train_evolutionary)(net, test, validation, 10, random_range = 1)
# best = benchmark(traingd_block)(net, test, validation, epochs, block_size = 10, stop_error_value = 0)

com = build_feedforward_committee(size=10, input_number=8, hidden_number=8, output_number=1)

print "Training evolutionary..."
benchmark(train_committee)(com, train_evolutionary, inputs, targets, epochs, random_range=1)

Y = com.sim(inputs)
area, best_cut = get_rocarea_and_best_cut(Y, targets)
[num_correct_first, num_correct_second, total_performance, num_first, num_second, missed] = stat(
    Y, targets, cut=best_cut
)
print (
    "Total number of data: " + str(len(targets)) + " (" + str(num_second) + " ones and " + str(num_first) + " zeros)"
)
print ("Number of misses: " + str(missed) + " (" + str(total_performance) + "% performance)")
print ("Specificity: " + str(num_correct_first) + "% (Success for class 0)")
print ("Sensitivity: " + str(num_correct_second) + "% (Success for class 1)")
print ("Roc Area: " + str(area) + "%")

save_committee(com, "/export/home/jonask/Projects/aNeuralN/ANNs/pimatrain_gen__rocarea" + str(area) + ".anncom")

print "\nTraining with gradient descent..."
benchmark(train_committee)(com, traingd, inputs, targets, epochs, block_size=10, stop_error_value=0)

Y = com.sim(inputs)