data = np.load('tmp/adv_mnist-28x28/fast-jsma_mnist-0.npy')
cnn = CNNModel(
image_size=28,
classes=10,
channels=1,
model_name='mnist-28x28',
model_dir='tmp/model-mnist-28x28',
conv_layers=[32, 64],
fc_layers=[1024],
)
x, _ = cnn.make_inputs()
probs = cnn.make_model(x)
cnn.start_session()
cnn.init_session_and_restore()
orig_correct_count = 0
success_count = 0
actual_success_count = 0
actual_label_count = 0
sample = data[0]
legit_preds = []
adv_preds = []
iteration = 1000
fgsm_params = {'eps': 0.5, 'clip_min': 0., 'clip_max': 1.}
gtsrb = GtsrbProvider()
# gtsrb.dump_images()
cnn = CNNModel(
image_size=gtsrb.IMAGE_SIZE,
classes=gtsrb.CLASSES,
model_name='gtsrb-64x64',
model_dir='tmp/gtsrb_model-64x64',
conv_layers=[32, 64, 128],
fc_layer=512,
)
x, y = cnn.make_inputs()
probs = cnn.make_model(x)
cnn.start_session()
fgsm = FastGradientMethod(cnn, sess=cnn.sess)
adv_x = fgsm.generate(x, **fgsm_params)
probs = cnn.make_model(adv_x)
cnn.adv_test(probs, x, y, adv_x, gtsrb.test_data(size=1000))
# cnn.test(gtsrb)
cnn.end_session()
#cnn.test(2000, gtsrb)
# for i in range(100):
# data, label = gtsrb.next_batch('test')
# print(data, label)