def generator(z, is_training=True, reuse=False):
# Network Architecture is exactly same as in infoGAN (https://arxiv.org/abs/1606.03657)
with tf.variable_scope("generator", reuse=reuse):
net = tf.nn.relu(
bn(linear(z, 1024, scope='g_fc1'),
is_training=is_training,
scope='linear1'))
net = tf.nn.relu(
bn(linear(net, 128 * 7 * 7, scope='g_fc2'),
is_training=is_training,
scope='linear2'))
net = tf.reshape(net, [-1, 7, 7, 128])
batch_size = net.get_shape().as_list()[0]
net = deconv2d(net,
output_size=14,
output_channel=64,
kernel=(4, 4),
stride=(2, 2),
activation='relu',
use_bn=True,
is_training=True,
name='d_conv1')
out = deconv2d(net,
output_size=28,
output_channel=1,
kernel=(4, 4),
stride=(2, 2),
activation='sigmoid',
name='gen_images')
return out
def __call__(self, x, y=None, sn=False, is_training=True, reuse=False):
with tf.variable_scope(self.name, reuse=reuse):
batch_size = x.get_shape().as_list()[0]
if y is not None:
ydim = y.get_shape().as_list()[-1]
y = tf.reshape(y, [batch_size, 1, 1, ydim])
x = conv_cond_concat(x, y) # [bz, 28, 28, 11]
x = tf.reshape(x, (batch_size, -1))
net = lrelu(dense(x, 512, sn=sn, name='d_fc1'), name='d_l1')
net = lrelu(bn(dense(net, 256, sn=sn, name='d_fc2'),
is_training,
name='d_bn2'),
name='d_l2')
net = lrelu(bn(dense(net, 128, sn=sn, name='d_fc3'),
is_training,
name='d_bn3'),
name='d_l3')
yd = dense(net, 1, sn=sn, name="D_dense")
if self.class_num:
yc = dense(net, self.class_num, sn=sn, name='C_dense')
return yd, net, yc
else:
return yd, net
def routePacket(data, dest):
ttl = bn(data[4]) - 1
if ttl > 0:
data[4] = nb(ttl, 1)
if dest in knownTable:
pckNum = bn(data[1:3])
sendPacket(data, knownTable[dest]['IP'], knownTable[dest]['PORT'], pckNum)
pass
def parseChunking(data):
if data[42:45] in chunkingHistory:
if chunkingHistory[data[42:45]]['chunkId'] + 1 == bn(data[45:53]):
chunkingHistory[data[42:45]]['chunkId'] += 1
chunkingHistory[data[42:45]]['message'] += data[53:]
if data[41] == '\x01':
result = chunkingHistory[data[42:45]]['message']
del chunkingHistory[data[42:45]]
return result
else:
chunkingHistory[data[42:45]] = {
'message': data[53:],
'chunkId': bn(data[45:53])
}
pass
def discriminator(x, is_training=True, reuse=False):
# Network Architecture is exactly same as in infoGAN (https://arxiv.org/abs/1606.03657)
batch_size = x.get_shape().as_list()[0]
with tf.variable_scope("discriminator", reuse=reuse):
net = conv2d(x,
output_dim=64,
kernel=(4, 4),
stride=(2, 2),
activation='lrelu',
name='conv1')
net = conv2d(net,
output_dim=128,
kernel=(4, 4),
stride=(2, 2),
activation='lrelu',
use_bn=True,
is_training=is_training,
name='conv2')
net = tf.reshape(net, [batch_size, -1])
net = lrelu(
bn(linear(net, 1024, scope='d_fc3'),
is_training=is_training,
scope='linear1'))
out_logit = linear(net, 1, scope='linear2')
out = tf.nn.sigmoid(out_logit)
return out, out_logit, net
def __call__(self, z, y=None, is_training=True, reuse=False):
with tf.variable_scope(self.name, reuse=reuse):
batch_size = z.get_shape().as_list()[0]
if y is not None:
z = tf.concat([z, y], 1)
net = tf.nn.relu(
bn(dense(z, 128, name='g_fc1'), is_training, name='g_bn1'))
net = tf.nn.relu(
bn(dense(net, 256, name='g_fc2'), is_training, name='g_bn2'))
net = tf.nn.relu(
bn(dense(net, 512, name='g_fc3'), is_training, name='g_bn3'))
net = tf.nn.relu(
bn(dense(net, 1024, name='g_fc4'), is_training, name='g_bn4'))
net = tf.nn.sigmoid(dense(net, 784, name='g_fc5'))
out = tf.reshape(net, (batch_size, 28, 28, 1))
return out
def __call__(self, x, y=None, sn=False, is_training=True, reuse=False):
with tf.variable_scope(self.name, reuse=reuse):
batch_size = x.get_shape().as_list()[0]
if y is not None:
ydim = y.get_shape().as_list()[-1]
y = tf.reshape(y, [batch_size, 1, 1, ydim])
x = conv_cond_concat(x, y) # [bz, 28, 28, 11]
# [bz, 14, 14, 64]
net = lrelu(conv2d(x,
64,
4,
4,
2,
2,
sn=sn,
padding="SAME",
name='d_conv1'),
name='d_l1')
# [bz, 7, 7, 128]
net = lrelu(bn(conv2d(net,
128,
4,
4,
2,
2,
sn=sn,
padding="SAME",
name='d_conv2'),
is_training,
name='d_bn2'),
name='d_l2')
net = tf.reshape(net, [batch_size, 7 * 7 * 128])
# [bz, 1024]
net = lrelu(bn(dense(net, 1024, sn=sn, name='d_fc3'),
is_training,
name='d_bn3'),
name='d_l3')
# [bz, 1]
yd = dense(net, 1, sn=sn, name='D_dense')
if self.class_num:
yc = dense(net, self.class_num, sn=sn, name='C_dense')
return yd, net, yc
else:
return yd, net
def __call__(self, x, is_training=True, reuse=False):
with tf.variable_scope(self.name, reuse):
net = lrelu(bn(dense(x, 64, name='c_fc1'),
is_training,
name='c_bn1'),
name='c_l1')
out_logit = dense(net, self.class_num, name='c_l2')
out = tf.nn.softmax(out_logit)
return out_logit, out
def __call__(self, z, y=None, is_training=True, reuse=False):
with tf.variable_scope(self.name, reuse=reuse):
batch_size = z.get_shape().as_list()[0]
if y is not None:
z = tf.concat([z, y], 1) # [bz,zdim+10]
net = tf.nn.relu(
bn(dense(z, 1024, name='g_fc1'), is_training, name='g_bn1'))
net = tf.nn.relu(
bn(dense(net, 128 * 7 * 7, name='g_fc2'),
is_training,
name='g_bn2'))
net = tf.reshape(net, [batch_size, 7, 7, 128])
# [bz, 14, 14, 64]
net = tf.nn.relu(
bn(deconv2d(net, 64, 4, 4, 2, 2, padding='SAME', name='g_dc3'),
is_training,
name='g_bn3'))
# [bz, 28, 28, 1]
out = tf.nn.sigmoid(
deconv2d(net, 1, 4, 4, 2, 2, padding='SAME', name='g_dc4'))
return out
def updateRoutingTable(message, source):
global updateTable, routingTable
if len(message) == 0 or message == '\x00':
routingTable[:] = [d for d in routingTable if d.get('nextHop') != source or d.get('destination') != source]
updateTable = True
retrieveRoutingTable()
else:
numberOfRoutes = len(message) / 17
routingTable[:] = [d for d in routingTable if d.get('nextHop') != source]
for x in xrange(numberOfRoutes):
route = message[17*x : 17 * (x+1)]
cost = bn(route[16])
email = route[:16]
if email != sourceAddress:
routingTable.append({
'destination': email,
'nextHop': source,
'metric': cost + 1
})
updateTable = True
retrieveRoutingTable()
pass
def parseMessage():
while True:
data, addr = sock.recvfrom(100)
# MESSAGE TYPE 02 = Data, 04 = ACK
if data[3] == '\x02':
source = data[8:24]
destination = data[24:40]
if sourceAddress != destination:
routePacket(data, destination)
if data[40] in ['\x05', '\x0D', '\x01', '\x09']:
message = ''
if data[40] in ['\x05', '\x0D']:
message = parseChunking(data)
else:
message = data[41:]
sendAck(data[1:3], source, addr[0], addr[1])
if message:
sendBack = False
if not any(d['nextHop'] == source for d in routingTable):
msg_list.insert(END, "Neighbor " + knownTable[source]['email'] + " connected")
neighbors.append(addr[0] + ":"+ str(addr[1]) + ":" + knownTable[source]['email'])
sendBack = True
if len(message) == 0 or message == '\x00':
neighborToRemove = addr[0] + ":" + str(addr[1]) + ":" + knownTable[source]['email']
idx = neighbors.index(neighborToRemove)
del neighbors[idx]
if data[40] in ['\x0D', '\x09']: message = Encryption.decrypt(message)
updateRoutingTable(message, source)
if sendBack:
sendRoutingTable(addr[0], addr[1], source)
elif data[40] in ['\x06', '\x0E', '\x02', '\x0A']:
message = None
if data[40] in ['\x06', '\x0E']:
message = parseChunking(data)
else:
message = data[41:]
sendAck(data[1:3], source, addr[0], addr[1])
if message is not None:
if data[40] in ['\x0E', '\x0A']: message = Encryption.decrypt(message)
messageType = message[0]
message = message[1:]
if messageType == '\x02':
f = open('output.file', 'wb')
f.write(message)
f.close()
continue
from_email = knownTable[source]['email']
msg_list.insert(END, from_email + ': ' + message)
elif data[3] == '\x04':
dest_addr = addr[0] + ":" + str(addr[1])
packetConfId = bn(data[5:7])
if dest_addr in ackHistory and packetConfId in ackHistory[dest_addr]:
del ackHistory[dest_addr][packetConfId]
pass
