def __init__(self, sess):
self._sess = sess
self._x = tf.placeholder(tf.float32, (1, 32, 32, 3))
self._encode = discretize_uniform(self._x / 255.0,
levels=LEVELS,
thermometer=True)
self._model = Model('../models/thermometer_advtrain/',
sess,
tiny=False,
mode='eval',
thermometer=True,
levels=LEVELS)
self._dataset = robustml.dataset.CIFAR10()
self._threat_model = robustml.threat_model.Linf(epsilon=8.0 / 255.0)
num_summary_steps = config['num_summary_steps']
num_checkpoint_steps = config['num_checkpoint_steps']
step_size_schedule = config['step_size_schedule']
weight_decay = config['weight_decay']
# data_path = config['data_path']
data_path = '../cifar10_data/'
momentum = config['momentum']
batch_size = config['training_batch_size']
levels = 16
# Setting up the data and the model
raw_cifar = cifar10_input.CIFAR10Data(data_path)
global_step = tf.contrib.framework.get_or_create_global_step()
model = Model(mode='eval', tiny=False, thermometer=False, levels=levels)
# Setting up the optimizer
boundaries = [int(sss[0]) for sss in step_size_schedule]
boundaries = boundaries[1:]
values = [sss[1] for sss in step_size_schedule]
learning_rate = tf.train.piecewise_constant(tf.cast(global_step, tf.int32),
boundaries, values)
total_loss = model.mean_xent + weight_decay * model.weight_decay_loss
train_step = tf.train.MomentumOptimizer(learning_rate, momentum).minimize(
total_loss, global_step=global_step)
xin = tf.placeholder(tf.float32, (None, 32, 32, 3))
steps = 7
eps = 0.031
max_num_training_steps = config['max_num_training_steps']
num_output_steps = config['num_output_steps']
num_summary_steps = config['num_summary_steps']
num_checkpoint_steps = config['num_checkpoint_steps']
step_size_schedule = config['step_size_schedule']
weight_decay = config['weight_decay']
data_path = config['data_path']
momentum = config['momentum']
batch_size = config['training_batch_size']
levels = 16
# Setting up the data and the model
raw_cifar = cifar10_input.CIFAR10Data(data_path)
global_step = tf.contrib.framework.get_or_create_global_step()
model = Model(mode='train', tiny=False, thermometer=True, levels=levels)
# Setting up the optimizer
boundaries = [int(sss[0]) for sss in step_size_schedule]
boundaries = boundaries[1:]
values = [sss[1] for sss in step_size_schedule]
learning_rate = tf.train.piecewise_constant(tf.cast(global_step, tf.int32),
boundaries, values)
total_loss = model.mean_xent + weight_decay * model.weight_decay_loss
train_step = tf.train.MomentumOptimizer(learning_rate, momentum).minimize(
total_loss, global_step=global_step)
xin = tf.placeholder(tf.float32, (None, 32, 32, 3))
steps = 7
eps = 0.031
parser.add_argument('--debug', action='store_true')
args = parser.parse_args()
provider = robustml.provider.CIFAR10(args.cifar_path)
# saver = tf.train.Saver(max_to_keep=3)
start = 0
end = 100
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
model = Model('./models/adv_train_clean/',
sess,
mode='eval',
tiny=False,
thermometer=False,
levels=levels)
# initialize data augmentation
input_xs = tf.placeholder(tf.float32, [None, 32, 32, 3])
real_logits = tf.nn.softmax(model(input_xs))
# saver.restore(sess,
# os.path.join("models/adv_train_clean", 'checkpoint-65000'))
for i in range(start, end):
x_batch, y_batch = provider[i]
logits = sess.run(real_logits, feed_dict={input_xs: [x_batch]})
# nat_dict = {model.x_input: [x_batch],model.y_input: [y_batch]}
nquery += 1
#print("size of image:",x0.shape)
#print("size of modifier,",np.array(lbd_mid*theta).shape )
if self.model.predict(x0 + np.array(lbd_mid * theta)) != y0:
lbd_hi = lbd_mid
else:
lbd_lo = lbd_mid
return lbd_hi, nquery
levels = 16
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
cifar = cifar10_input.CIFAR10Data()
model = Model('../models/thermometer_advtrain/',
sess,
tiny=False,
mode='eval',
thermometer=True,
levels=levels)
model = MyModel(model, sess, levels, [0.0, 255.0])
images = np.array(cifar.eval_data.xs[:100], dtype=np.float32)
labels = cifar.eval_data.ys[:100]
new_img = images / 255.0
count = []
for i in range(20):
label = model.predict(new_img[i])
if label == labels[i]:
count.append(1)
else:
lbd_hi = lbd
lbd_lo = 0.0
while (lbd_hi - lbd_lo) > 1e-5:
lbd_mid = (lbd_lo + lbd_hi) / 2.0
nquery += 1
if self.model.predict_label(x0 + np.array(lbd_mid * theta)) != y0:
lbd_hi = lbd_mid
else:
lbd_lo = lbd_mid
return lbd_hi, nquery
cifar = cifar10_input.CIFAR10Data("../cifar10_data")
sess = tf.InteractiveSession()
orig_model = Model("../models/standard/", tiny=False, mode='eval', sess=sess)
model = PyModel(orig_model, sess, [0.0, 255.0])
images = cifar.eval_data.xs[20:1000] / 255
labels = cifar.eval_data.ys[20:1000]
pre_labs = []
count = 0
for i in range(20):
pre_lab = model.predict_label(images[i])
pre_labs.append(pre_lab)
if labels[i] == pre_lab:
count += 1
print("original labels:", labels[:20])
print("predicted labels:", pre_labs)
print("accuracy of 20 images :", count / 20)
#count = 0
#pre_labs = []
import keras
import imageio
import tensorflow as tf
num_classes = 10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
x_test_tensor = K.variable(x_test)
model = Model(input_shape=x_train.shape[1:], num_classes=num_classes).model
wrap = KerasModelWrapper(model)
target = keras.utils.to_categorical([0], num_classes)
target = np.repeat(target, 10000, axis=0)
fgsm_params = {
'eps': 0.05,
'clip_min': 0.,
'clip_max': 1.,
'y_target': target
}
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
fgsm = FastGradientMethod(wrap, sess=sess)
# The data, split between train and test sets:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
model = Model(input_shape=x_train.shape[1:], num_classes=num_classes).model
callbacks = [
ModelCheckpoint('models/cifar10.h5',
save_best_only=True,
save_weights_only=True)
]
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
callbacks=callbacks,
shuffle=True)