def build_graph(cfg):
class _Dummy:
pass
env = _Dummy()
env.x = tf.placeholder(tf.int32, [cfg.batch_size, cfg.charlen], 'x')
env.y = tf.placeholder(tf.int32, [cfg.batch_size, 1], 'y')
env.training = tf.placeholder_with_default(False, (), 'mode')
m = CharLSTM(cfg)
env.model = m
env.ybar = m.predict(env.x, env.training)
env.saver = tf.train.Saver()
env = build_metric(env, cfg)
with tf.variable_scope('deepfool'):
env.adv_epochs = tf.placeholder(tf.int32, (), name='adv_epochs')
env.adv_eps = tf.placeholder(tf.float32, (), name='adv_eps')
xadv = fgm(m,
env.x,
epochs=env.adv_epochs,
eps=env.adv_eps,
sign=cfg.sign,
clip_min=-10,
clip_max=10)
env.xadv = m.reverse_embedding(xadv)
return env
def build_graph(cfg):
class _Dummy:
pass
env = _Dummy()
env.x = tf.placeholder(tf.int32, [None, cfg.seqlen + 1], 'x')
env.y = tf.placeholder(tf.int32, [None, 1], 'y')
env.training = tf.placeholder_with_default(False, (), 'mode')
m = WordCNN(cfg)
env.ybar = m.predict(env.x, env.training)
env.model = m
# we do not save the embedding here since embedding is not trained.
env.saver = tf.train.Saver(var_list=m.varlist)
env = build_metric(env, cfg)
with tf.variable_scope('adv'):
env.adv_epochs = tf.placeholder(tf.int32, (), name='epochs')
env.adv_eps = tf.placeholder(tf.float32, (), name='eps')
env.xadv = fgm(m,
env.x,
epochs=env.adv_epochs,
eps=env.adv_eps,
sign=cfg.sign,
clip_min=-10,
clip_max=10)
return env
def __init__(self, input_dims, hidden_neurons, eps=0.1, pfp=0.5):
self.input_dims = input_dims
self.hidden_neurons = hidden_neurons
self.eps = eps
self.pfp = pfp
self.new = []
self.data_t = None
tf.reset_default_graph()
with tf.variable_scope('model'):
self.x = tf.placeholder(tf.float32, (None, input_dims), name='x')
self.y = tf.placeholder(tf.float32, (None, 1), name='y')
self.y_pred = self.simple_mlp(self.x)
# calculate the accuracy
with tf.variable_scope('acc'):
self.y_ = tf.where(tf.greater(self.y_pred, 0.5),
tf.ones_like(self.y_pred),
tf.zeros_like(self.y_pred))
count = tf.equal(self.y, self.y_)
self.acc = tf.reduce_mean(tf.cast(count, tf.float32),
name='acc')
with tf.variable_scope('loss'):
self.entropy_loss = -tf.reduce_mean(
self.y *
tf.log(tf.clip_by_value(self.y_pred, 1e-10, 1.0)) +
(1 - self.y) *
tf.log(tf.clip_by_value(1 - self.y_pred, 1e-10, 1.0)))
self.reg_loss = tf.add_n(
tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
self.loss = self.entropy_loss + self.reg_loss
with tf.variable_scope('train_op'):
optimizer = tf.train.AdamOptimizer(0.001)
self.train_op = optimizer.minimize(self.loss)
with tf.variable_scope('model', reuse=True):
self.fgm_eps = tf.placeholder(tf.float32, (), name='fgm_eps')
self.x_fgm = fgm(self.simple_mlp,
self.x,
epochs=1,
eps=self.fgm_eps)
self.sess = tf.InteractiveSession()
self.sess.run(tf.global_variables_initializer())
self.sess.run(tf.local_variables_initializer())
#tf.add_to_collection('loss', weight_decay)
with tf.variable_scope('train_op'):
#optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9)
optimizer = tf.train.AdamOptimizer()
env.train_op = optimizer.minimize(env.loss)
env.saver = tf.train.Saver()
with tf.variable_scope('model', reuse=True):
env.adv_eps = tf.placeholder(tf.float32, (), name='adv_eps')
env.adv_epochs = tf.placeholder(tf.int32, (), name='adv_epochs')
env.adv_y = tf.placeholder(tf.int32, (), name='adv_y')
env.x_fgsm = fgm(model, env.x, epochs=env.adv_epochs, eps=env.adv_eps)
env.x_deepfool = deepfool(model, env.x, epochs=env.adv_epochs, batch=True)
env.x_jsma = jsma(model,
env.x,
env.adv_y,
eps=env.adv_eps,
epochs=env.adv_epochs)
print('\nInitializing graph')
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
def evaluate(sess, env, X_data, y_data, batch_size=128):
def get_adv_dataset(sess, logits, x, y, x_test, y_test):
return sess.run(attacks.fgm(x, logits, eps=0.2, ord=np.inf, clip_min=0, clip_max=1, targeted=False),
feed_dict={x: x_test, y: y_test})
logits=logits)
env.loss = tf.reduce_mean(xent, name='loss')
with tf.variable_scope('train_op'):
# optimizer = tf.train.AdamOptimizer()
optimizer = tf.train.GradientDescentOptimizer(0.01)
env.train_op = optimizer.minimize(env.loss)
env.saver = tf.train.Saver()
with tf.variable_scope('model', reuse=True):
env.fgsm_eps = tf.placeholder(tf.float32, (), name='fgsm_eps')
env.fgsm_epochs = tf.placeholder(tf.int32, (), name='fgsm_epochs')
with slim.arg_scope(inception_resnet_v2_arg_scope()):
env.x_fgsm = fgm(cifarnet,
env.x,
epochs=env.fgsm_epochs,
eps=env.fgsm_eps)
print('\nInitializing graph')
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
def evaluate(sess, env, X_data, y_data, batch_size=128):
"""
Evaluate TF model by running env.loss and env.acc.
"""
print('\nEvaluating')
