def main(args):
if args.model == 'LSTM':
model = LSTM(input_dim=args.input_dim, lstm_hidden_dim=args.lstm_hidden_dim, time_step=args.time_step)
else:
raise ValueError
model.cuda()
if args.optim == 'SGD':
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
elif args.optim == 'SGD_momentum':
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, weight_decay=args.weight_decay)
elif args.optim == 'Adagrad':
optimizer = torch.optim.Adagrad(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
elif args.optim == 'RMSprop':
optimizer = torch.optim.RMSprop(model.parameters(), lr=args.lr, alpha=0.999, eps=1e-8, weight_decay=args.weight_decay)
elif args.optim == 'Adam':
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.999), eps=1e-8, weight_decay=args.weight_decay)
else:
raise ValueError
lr_scheduler = None
if args.load_path:
if args.recover:
load_model(model, args.load_path, strict=True)
print('load model state dict in {}'.format(args.load_path))
map_file_path = 'divide.csv'
data_file_path = 'processed_data.txt'
social_economical_path = '2010-2016.csv'
if args.dataset == 'NaiveDataset':
train_set = NaiveDataset(data_file_path, map_file_path)
elif args.dataset == 'AdvancedDataset':
train_set = AdvancedDataset(data_file_path, map_file_path, social_economical_path)
else:
raise ValueError
train_dataloader = DataLoader(train_set, batch_size=args.batch_size,
shuffle=True, num_workers=args.num_workers,
pin_memory=True)
if args.evaluate:
validate(train_dataloader, model)
return
train(train_dataloader, train_dataloader, model, optimizer, lr_scheduler, args)
# list of uppercase letters to init bandnames
uppers = string.ascii_uppercase
rnn_type = str(sys.argv[1])
if rnn_type == 'milstm':
decoder = miLSTM(n_characters, hidden_size, 64, n_characters)
elif rnn_type == 'lstm':
decoder = LSTM(n_characters, hidden_size, 64, n_characters)
else:
decoder = LN_miLSTM(n_characters, hidden_size, 64, n_characters)
decoder_optimizer = torch.optim.Adam(decoder.parameters(), lr=lr)
criterion = nn.CrossEntropyLoss()
decoder.cuda()
death_metal_bands = pd.read_csv('../data/death-metal/bands.csv')
band_raw = death_metal_bands['name'].tolist()
band_nms = []
for i, bnd in enumerate(band_raw):
band_nms.append(bnd + '<EOS>')
print('Found', len(band_nms), 'bands!')
def time_since(since):
s = time.time() - since
m = math.floor(s / 60)
