def main():
mlp_hiddens = [1000]
filter_sizes = [(9, 9), (5, 5), (5, 5)]
feature_maps = [80, 50, 20]
pooling_sizes = [(3, 3), (2, 2), (2, 2)]
save_to = "DvC.pkl"
image_size = (128, 128)
output_size = 2
learningRate = 0.1
num_epochs = 300
num_batches = None
if socket.gethostname() == 'tim-X550JX':
host_plot = 'http://*****:*****@ %s' %
('CNN ', datetime.datetime.now(), socket.gethostname()),
channels=[['train_error_rate', 'valid_error_rate'],
['train_total_gradient_norm']],
after_epoch=True,
server_url=host_plot))
model = Model(cost)
main_loop = MainLoop(algorithm,
stream_data_train,
model=model,
extensions=extensions)
main_loop.run()
# switch models
if args.model == 'lenet':
cnn = LeNet(enable_lat=args.enable_lat,
epsilon=args.epsilon,
pro_num=args.pro_num,
batch_size=args.batchsize,
batch_norm=args.batchnorm,
if_dropout=args.dropout)
elif args.model == 'resnet':
cnn = ResNet50(enable_lat=args.enable_lat,
epsilon=args.epsilon,
pro_num=args.pro_num,
batch_size=args.batchsize,
if_dropout=args.dropout)
cnn.apply(conv_init)
elif args.model == 'vgg':
cnn = VGG16(enable_lat=args.enable_lat,
epsilon=args.epsilon,
pro_num=args.pro_num,
batch_size=args.batchsize,
if_dropout=args.dropout)
elif args.model == 'alexnet':
cnn = AlexNet(enable_lat=args.enable_lat,
epsilon=args.epsilon,
pro_num=args.pro_num,
batch_size=args.batchsize,
if_dropout=args.dropout)
cnn.cuda()
if os.path.exists(real_model_path):
optimizer.step()
return self.net.state_dict()
def weight_init(m):
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
net = LeNet().to(device)
net.apply(weight_init)
optimizer_server = optim.SGD(net.parameters(), lr=LR, momentum=0.9)
client_list = []
# train_data_root = '/home/dchen/dataset/MNIST/IID/' + str(CLIENT_NUM) + '/train/'
# test_data_root = '/home/dchen/dataset/MNIST/IID/' + str(CLIENT_NUM) + '/test/'
train_data_root = '/home/dchen/dataset/MNIST/Non-IID1/' + str(CLIENT_NUM) + '/train/'
test_data_root = '/home/dchen/dataset/MNIST/Non-IID1/' + str(CLIENT_NUM) + '/test/'
for i in range(CLIENT_NUM):
client_name = 'client' + str(i)
client_list.append(Client(client_name, train_data_root + client_name + '/', test_data_root + client_name + '/'))
center_params_dict = dict()
center_params_modules = list(self.net.named_parameters())
def main():
mlp_hiddens = [1000]
filter_sizes = [(9,9),(5,5),(5,5)]
feature_maps = [80, 50, 20]
pooling_sizes = [(3,3),(2,2),(2,2)]
save_to="DvC.pkl"
image_size = (128, 128)
output_size = 2
learningRate=0.1
num_epochs=300
num_batches=None
if socket.gethostname()=='tim-X550JX':host_plot = 'http://*****:*****@ %s' % ('CNN ', datetime.datetime.now(), socket.gethostname()),
channels=[['train_error_rate', 'valid_error_rate'],
['train_total_gradient_norm']], after_epoch=True, server_url=host_plot))
model = Model(cost)
main_loop = MainLoop(
algorithm,
stream_data_train,
model=model,
extensions=extensions)
main_loop.run()
def weight_init(m):
# 使用isinstance来判断m属于什么类型
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
# torch.manual_seed(7) # 随机种子，是否每次做相同初始化赋值
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
# m中的 weight，bias 其实都是 Variable，为了能学习参数以及后向传播
m.weight.data.fill_(1)
m.bias.data.zero_()
net = LeNet()
attacker_net = LeNet()
# 初始化网络参数
net.apply(weight_init) # apply函数会递归地搜索网络内的所有module并把参数表示的函数应用到所有的module上
attacker_net.apply(weight_init)
# # 提取网络参数
# net_dic = net.state_dict()
# 定义损失函数
criterion = nn.CrossEntropyLoss() # 交叉熵损失函数，通常用于多分类问题上
optimizer_server = optim.Adam(net.parameters(), lr=LR, betas=(0.9, 0.99))
optimizer_backdoor = optim.Adam(attacker_net.parameters(),
lr=LR,
betas=(0.9, 0.99))
# 分配用户参数 send_back()
client_0_net = LeNet()
client_1_net = LeNet()
client_2_net = LeNet()
# client_0_net.load_state_dict(net_dic)
def main(port_data):
mlp_hiddens = [500]
filter_sizes = [(3,3),(3,3)]
feature_maps = [20, 20]
pooling_sizes = [(3,3),(2,2)]
save_to="DvC.pkl"
image_size = (128, 128)
output_size = 2
learningRate=0.1
num_epochs=300
num_batches=None
if socket.gethostname()=='tim-X550JX':host_plot = 'http://*****:*****@ %s' % ('CNN ', datetime.datetime.now(), socket.gethostname()),
channels=[['train_error_rate', 'valid_error_rate'],
['train_total_gradient_norm']], after_epoch=True, server_url=host_plot))
model = Model(cost)
main_loop = MainLoop(
algorithm,
stream_data_train,
model=model,
extensions=extensions)
main_loop.run()
def main(port_data):
mlp_hiddens = [500]
filter_sizes = [(3, 3), (3, 3)]
feature_maps = [20, 20]
pooling_sizes = [(3, 3), (2, 2)]
save_to = "DvC.pkl"
image_size = (128, 128)
output_size = 2
learningRate = 0.1
num_epochs = 300
num_batches = None
if socket.gethostname() == 'tim-X550JX':
host_plot = 'http://*****:*****@ %s' %
('CNN ', datetime.datetime.now(), socket.gethostname()),
channels=[['train_error_rate', 'valid_error_rate'],
['train_total_gradient_norm']],
after_epoch=True,
server_url=host_plot))
model = Model(cost)
main_loop = MainLoop(algorithm,
stream_data_train,
model=model,
extensions=extensions)
main_loop.run()