parser.add_argument('-gpus',
default=[],
nargs='+',
type=int,
help="Use CUDA on the listed devices.")
parser.add_argument('-corpus',
default="../data/small",
type=str,
help="corpus to train the model")
opt = parser.parse_args()
device = torch.device("cpu")
if len(opt.gpus) != 0:
torch.cuda.set_device(opt.gpus[0])
device = torch.device("cuda:" + str(opt.gpus[0]))
data_set = LoadData.DataSet(opt.corpus)
num_data = len(data_set)
print("Training instance:", num_data)
dl = torch.utils.data.DataLoader(data_set,
batch_size=opt.batch_size,
shuffle=True,
num_workers=0)
criterion = nn.CrossEntropyLoss().to(device)
epochs = opt.epochs
network = Net.CBOWNet(20000, opt.embed_dimension).to(device)
print(device)
optimizer = torch.optim.Adam(network.parameters())
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
best_loss = 1000000.0
for i in range(epochs):
