def main(): train_data, val_data, test_data = data.data_loader() #eta2 = np.abs([np.random.randn(sizes[0],1)]) eta2 =np.abs([ 0.8 for x in range(sizes[0])]) p1 = map(add,test_data[0][0],eta2) p2 = map(add,test_data[1][0],eta2) p = [(p1,test_data[0][1]), (p2,test_data[1][1])] q1 = test_data[0][0] q2 = test_data[1][0] q = [(q1,test_data[0][1]), (q2,test_data[1][1])] qq = np.reshape(q1,(28,28)) plt.imshow(qq, cmap = cm.Greys_r) plt.show() pp = np.reshape(p1,(28,28)) plt.imshow(pp, cmap = cm.Greys_r) plt.show() #plt.imshow(pp, cmap = cm.Greys_r) #plt.show() SGD(train_data, epochs, mini_batch_size, eta, val_data) n_test = len(p) print "Original result {0} / {1}".format(evaluate(q), n_test) print "Adversarial {0} / {1}".format(evaluate(p), n_test)
def main(): train_data, val_data, test_data = data.data_loader() SGD(train_data, epochs, mini_batch_size, eta, test_data)