def get_result(self):
print('processing ' + os.path.split(self.dst_output_path)[-1])
list_feature_file_path = os.path.join(self.dst_output_path,
'features_379' + '.npy')
dense_adj_mat_path = os.path.join(self.dst_output_path,
'adj_matrix.npy')
dense_node_feat_path = os.path.join(self.dst_output_path,
'node_features.npy')
adj_mat_path = os.path.join(self.dst_output_path, 'adj_matrix.npz')
node_feat_path = os.path.join(self.dst_output_path,
'node_features.npz')
if os.path.exists(list_feature_file_path) and os.path.exists(
adj_mat_path) and os.path.exists(node_feat_path):
return STATUS_OK
if os.path.exists(list_feature_file_path) and os.path.exists(
dense_adj_mat_path) and os.path.exists(dense_node_feat_path):
sp.save_npz(adj_mat_path,
dense_to_sparse(np.load(dense_adj_mat_path)))
sp.save_npz(node_feat_path,
dense_to_sparse(np.load(dense_node_feat_path)))
os.remove(dense_adj_mat_path)
os.remove(dense_node_feat_path)
return STATUS_OK
if self.__check_prerequisites() != STATUS_OK:
print('prerequisites not satisfied')
return STATUS_ERR
if self.include_permissions_147 and self.__fetch_permissions(
) != STATUS_OK:
print('fetch permissions failed')
return STATUS_ERR
if (self.include_intent_actions_126 or self.include_intent_actions_110
) and self.__fetch_intent_actions() != STATUS_OK:
print('fetch intent actions failed')
return STATUS_ERR
if self.include_sensitive_apis_106 and self.__fetch_sensitive_apis(
) != STATUS_OK:
print('fetch sensitive apis failed')
return STATUS_ERR
np.save(list_feature_file_path,
np.array(self.feature_list, dtype=np.uint8))
print('processing ' + os.path.split(self.dst_output_path)[-1])
list_feature_file_path = os.path.join(self.dst_output_path,
'features_379' + '.npy')
if os.path.exists(list_feature_file_path):
return STATUS_OK
else:
return STATUS_ERR
def _build_train_op(self, logits, Y):
# self.global_step = tf.Variable(0, trainable=False)
Y = utils.dense_to_sparse(Y)
loss = tf.nn.ctc_loss(
labels=Y,
inputs=logits,
sequence_length=self.seq_len,
# preprocess_collapse_repeated = True,
# ctc_merge_repeated=False,
)
#all_vars = tf.trainable_variables()
#lossL2 = tf.add_n([ tf.nn.l2_loss(v) for v in all_vars ]) * FLAGS.decay_weight
#self.loss = tf.reduce_mean(loss) + lossL2
self.loss = tf.reduce_mean(loss)
tf.summary.scalar('loss', self.loss)
tf.summary.scalar('learning_rate', self.lrn_rate)
# self.optimizer = tf.train.RMSPropOptimizer(learning_rate=self.lrn_rate,
# momentum=FLAGS.momentum).minimize(self.cost,
# global_step=self.global_step)
# self.optimizer = tf.train.MomentumOptimizer(learning_rate=self.lrn_rate,
# momentum=FLAGS.momentum,
# use_nesterov=True).minimize(self.cost,
# global_step=self.global_step)
grads = self.opt.compute_gradients(self.loss)
# Option 2: tf.nn.ctc_beam_search_decoder
# (it's slower but you'll get better results)
decoded, log_prob = \
tf.nn.ctc_beam_search_decoder(logits,
self.seq_len,
beam_width=5,
merge_repeated=False)
dense_decoded = tf.sparse_tensor_to_dense(decoded[0], default_value=-1)
return dense_decoded, grads
def __init__(self, A, n_features, hidden_dim=32, code_dim=16):
super().__init__()
self.hidden = l.KipfAndWillingConv(n_features, hidden_dim)
self.encoder = l.KipfAndWillingConv(hidden_dim, code_dim)
transform = l.KipfAndWillingConv.compute_transform(A)
self.transform = u.dense_to_sparse(transform)
def __init__(self, A, n_features, n_samples, hidden_dim=32, code_dim=16):
super().__init__()
self.hidden = l.KipfAndWillingConv(n_features, hidden_dim)
self.means_encoder = l.KipfAndWillingConv(hidden_dim, code_dim)
self.log_std_encoder = l.KipfAndWillingConv(hidden_dim, code_dim)
transform = l.KipfAndWillingConv.compute_transform(A)
self.transform = u.dense_to_sparse(transform)
self.n_samples = A.shape[0]
def __build_node_features(self):
# Read the adjacency matrix and list of functions
all_funcs = []
read_file_to_list(all_funcs,
os.path.join(self.dst_output_path, 'all_funcs.txt'))
node_features = []
for i in range(len(all_funcs)):
feature_list = []
get_filtered_vector(
feature_list, all_funcs,
CONSTANTS['SENSITIVE_APIS_106']['REFERENCE_LIST'])
node_features.append(feature_list)
sp.save_npz(
os.path.join(self.dst_output_path, 'node_features_api.npz'),
dense_to_sparse(np.array(node_features)))