# adv = Attacks.new_mi_fgsm(model, img, labels[i], eps=20, T=25)
# #adv = Attacks.fgsm(model, img, labels[i], eps=20)
# pred = model.predict(adv)
# print(letters[pred.argmax()], pred.max() * 100)
# plt.imsave("./images/m_{}.png".format(i),
# adv.reshape(128, 128),
# cmap="gray")
with tf.compat.v1.Session() as sess:
#data = images / 255.0
#data = img.reshape((-1, 128, 128, 1))
data = images
#data, model = MNIST(), MNISTModel("models/mnist", sess)
attack = Attacks.CarliniL0(sess,
model,
max_iterations=100,
initial_const=10,
largest_const=15)
inputs, targets = generate_data(data,
samples=1,
targeted=True,
start=0,
inception=False)
timestart = time.time()
adv = attack.attack(inputs, targets)
timeend = time.time()
print("Took", timeend - timestart, "seconds to run", len(inputs),
"samples.")
