def run():
args = NGCF_Args(parse_args())
# 获取训练集的dataloader形式
data = DATA(args.data_path, args.dataset_name)
train_set, train_U2I, test_U2I, edge_indices, edge_weight, n_users, n_items = data.load(
)
train_dl = get_loader(train_set, train_U2I, n_items, args.batch_size,
args.cores)
# 获取归一化的拉普拉斯矩阵
laplace_graph = Graph(edge_indices, edge_weight)
laplace_graph.add_self_loop()
laplace_graph.norm()
norm_adj = laplace_graph.mat.cuda()
# 定义网络
model = NGCF(n_users, n_items, norm_adj, args)
model = model.cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
# 定义会话
sess = Session(model)
for epoch in range(args.num_epochs):
loss, mf_loss, emb_loss = sess.train(train_dl, optimizer)
print("epoch: {:d}, loss = [{:.6f} == {:.6f} + {:.6f}]".format(
epoch, loss, mf_loss, emb_loss))
perf_info = evaluate(model, n_users, n_items, train_U2I, test_U2I,
args)
print("precision: [{:.6f}] recall: [{:.6f}] ndcg: [{:.6f}]".format(
perf_info[0], perf_info[1], perf_info[2]))
def run():
args = BPRMF_Args(parse_args())
# 获取训练集的dataloader形式
data = DATA(args.data_path, args.dataset_name)
train_set, train_U2I, test_U2I, n_users, n_items = data.load()
train_dl = get_loader(train_set, train_U2I, n_items, args.batch_size,
args.cores)
# 定义网络
model = BPRMF(n_users, n_items, args)
model = model.cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
# 定义会话
sess = Session(model)
for epoch in range(args.num_epochs):
loss, mf_loss, emb_loss = sess.train(train_dl, optimizer)
print("epoch: {:d}, loss = [{:.6f} == {:.6f} + {:.6f}]".format(
epoch, loss, mf_loss, emb_loss))
perf_info = evaluate(model, n_users, n_items, train_U2I, test_U2I,
args)
print("precision: [{:.6f}] recall: [{:.6f}] ndcg: [{:.6f}]".format(
perf_info[0], perf_info[1], perf_info[2]))
from dataset import MRIDataset as DATA
else:
from dataset import MRIDataset_threechannel as DATA
if args.network == 'Inception_v3':
from Inception_v3 import inception_v3_pretrain as MODEL
Transform = transforms.Compose(
[transforms.Resize((SIZE, SIZE)),
transforms.ToTensor()])
if __name__ == '__main__':
# writer = SummaryWriter(path_to_logs_dir)
dataset = DATA(path_to_data,
path_to_label,
mode=MODE,
transform=Transform,
aug=True)
weight = 1. / torch.tensor([dataset.negative, dataset.positive],
dtype=torch.float)
target = torch.tensor(dataset._label['label'], dtype=torch.long)
sample_weight = torch.tensor([weight[t] for t in target],
dtype=torch.float)
sampler = WeightedRandomSampler(sample_weight, len(sample_weight))
dataloader = DataLoader(dataset,
Batch_size,
sampler=sampler,
num_workers=1,
drop_last=True)
dataset_test = DATA(path_to_testdata,