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.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 = deepfool(m,
env.x,
epochs=env.adv_epochs,
eps=env.adv_eps,
batch=True,
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('deepfool'):
env.adv_epochs = tf.placeholder(tf.int32, (), name='adv_epochs')
env.adv_eps = tf.placeholder(tf.float32, (), name='adv_eps')
env.xadv = deepfool(m,
env.x,
epochs=env.adv_epochs,
eps=env.adv_eps,
batch=True,
clip_min=-10,
clip_max=10)
return env
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)
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)
return env
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('train_op'):
optimizer = tf.train.AdamOptimizer()
env.train_op = optimizer.minimize(env.loss)
return env
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('hotflip'):
env.xadv = hf_replace(m, env.x, seqlen=cfg.charlen,
embedding_dim=cfg.embedding_dim,
beam_width=cfg.beam_width, chars=cfg.maxchars)
return env