def create_model(args, model_name, output_dim):
logging.info("create_model. model_name = %s, output_dim = %s" %
(model_name, output_dim))
model = None
if model_name == "lr" and args.dataset == "mnist":
logging.info("LogisticRegression + MNIST")
model = LogisticRegression(28 * 28, output_dim)
elif model_name == "cnn" and args.dataset == "femnist":
logging.info("CNN + FederatedEMNIST")
model = CNN_DropOut(False)
elif model_name == "resnet18_gn" and args.dataset == "fed_cifar100":
logging.info("ResNet18_GN + Federated_CIFAR100")
model = resnet18()
elif model_name == "rnn" and args.dataset == "shakespeare":
logging.info("RNN + shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "rnn" and args.dataset == "fed_shakespeare":
logging.info("RNN + fed_shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "lr" and args.dataset == "stackoverflow_lr":
logging.info("lr + stackoverflow_lr")
model = LogisticRegression(10004, output_dim)
elif model_name == "cnn" and args.dataset == "stackoverflow_nwp":
logging.info("CNN + stackoverflow_nwp")
model = RNN_StackOverFlow()
return model
def main():
ROOT_PATH = args.root_path
RUN_NAME = str(args.mode) + \
"-id" + str(args.run_id) + \
"-group_id" + str(args.group_id) + \
"-n" + str(args.client_number) + \
"-symm" + str(args.b_symmetric) + \
"-tu" + str(args.topology_neighbors_num_undirected) + \
"-td" + str(args.topology_neighbors_num_directed) + \
"-lr" + str(args.learning_rate),
wandb.init(project="fedml", name=str(RUN_NAME), config=args)
logging.basicConfig(
level=logging.INFO,
format=
' - %(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
datefmt='%a, %d %b %Y %H:%M:%S')
logging.info('Decentralized Online Learning.')
# fix random seeds
seed = 1234
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
# create id list
client_number = args.client_number
client_id_list = [i for i in range(client_number)]
print(client_id_list)
# load data
iteration_number_T = args.iteration_number
sample_num_in_total = client_number * iteration_number_T
beta = args.beta
data_name = args.data_name
data_path = ""
if data_name == "SUSY":
data_path = ROOT_PATH + "SUSY/SUSY.csv"
input_dim = 18
elif data_name == "RO":
data_path = ROOT_PATH + "room_occupancy/datatraining.txt"
input_dim = 5
else:
input_dim = 5
data_loader = DataLoader(data_name, data_path, client_id_list,
sample_num_in_total, beta)
streaming_data = data_loader.load_datastream()
# create model
model = LogisticRegression(input_dim, args.output_dim)
model_cache = LogisticRegression(input_dim, args.output_dim)
# start training
FedML_decentralized_fl(client_number, client_id_list, streaming_data,
model, model_cache, args)
def main():
# test the data loader
# Hyper Parameters
input_size = 784
num_classes = 10
num_epochs = 10
batch_size = 10
learning_rate = 0.03
np.random.seed(0)
torch.manual_seed(10)
device = torch.device("cuda:0")
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_mnist(batch_size)
model = LogisticRegression(input_size, num_classes).to(device)
# Loss and Optimizer
# Softmax is internally computed.
# Set parameters to be updated.
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
# Training the Model
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_data_global):
images = images.to(device)
labels = labels.to(device)
# Forward + Backward + Optimize
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if (i + 1) % 100 == 0:
print('Epoch: [%d/%d], Step: [%d/%d], Loss: %.4f' %
(epoch + 1, num_epochs, i + 1, len(train_data_global),
loss.item()))
# Test the Model
correct = 0
total = 0
for images, labels in test_data_global:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
# 86%
print('Accuracy of the model on the 7371 test images: %d %%' %
(100 * correct // total))
def create_model(args, model_name, output_dim):
model = None
if model_name == "lr" and args.dataset == "mnist":
model = LogisticRegression(28 * 28, output_dim)
args.client_optimizer = 'sgd'
elif model_name == "rnn" and args.dataset == "shakespeare":
model = RNN_OriginalFedAvg(28 * 28, output_dim)
args.client_optimizer = 'adam'
elif model_name == "resnet56":
model = resnet56(class_num=output_dim)
elif model_name == "mobilenet":
model = mobilenet(class_num=output_dim)
return model
def create_model(args, model_name, output_dim):
logging.info("create_model. model_name = %s, output_dim = %s" %
(model_name, output_dim))
model = None
if model_name == "lr" and args.dataset == "mnist":
logging.info("LogisticRegression + MNIST")
model = LogisticRegression(28 * 28, output_dim)
elif model_name == "rnn" and args.dataset == "shakespeare":
logging.info("RNN + shakespeare")
model = RNN_OriginalFedAvg(28 * 28, output_dim)
elif model_name == "cnn" and args.dataset == "femnist":
logging.info("CNN + FederatedEMNIST")
model = CNN_OriginalFedAvg(False)
return model