exp_time = "{0:%Y-%m-%d}_{0:%H-%M-%S}".format(datetime.now())
SAVEDIR = os.path.join('./saves', 'copytask', NET_TYPE, str(random_seed),
exp_time)
torch.cuda.manual_seed(random_seed)
torch.manual_seed(random_seed)
np.random.seed(random_seed)
inp_size = 1
T = args.T
batch_size = args.batch
out_size = args.labels + 1
if args.onehot:
inp_size = args.labels + 2
rnn = select_network(args, inp_size)
net = Model(hidden_size, rnn)
if CUDA:
net = net.cuda()
net.rnn = net.rnn.cuda()
print('Copy task')
print(NET_TYPE)
print('Cuda: {}'.format(CUDA))
print(nonlin)
print(hidden_size)
for name, param in net.named_parameters():
if param.requires_grad:
print(name, param.data)
if not os.path.exists(SAVEDIR):
os.makedirs(SAVEDIR)
val_data = batchify(corpus.valid, eval_batch_size)
test_data = batchify(corpus.test, eval_batch_size)
###############################################################################
# Build the model
###############################################################################
ntokens = len(corpus.dictionary)
NET_TYPE = args.net_type
inp_size = args.emsize
hid_size = args.nhid
alam = args.alam
CUDA = args.cuda
nonlin = args.nonlin
rnn = select_network(inp_size, args)
model = RNNModel(rnn, ntokens, inp_size, hid_size, args.tied)
if args.cuda:
model.cuda()
print('Language Task')
print(NET_TYPE)
print(args)
criterion = nn.CrossEntropyLoss()
###############################################################################
# Training code
###############################################################################
NET_TYPE = args.net_type
CUDA = args.cuda
decay = args.weight_decay
hidden_size = args.nhid
torch.cuda.manual_seed(random_seed)
torch.manual_seed(random_seed)
np.random.seed(random_seed)
inp_size = 1
T = args.T
batch_size = args.batch
out_size = args.labels + 1
if args.onehot:
inp_size = args.labels + 2
rnn = select_network(NET_TYPE, inp_size, hidden_size, nonlin, args.rinit,
args.iinit, CUDA, args.lastk, args.rsize)
net = Model(hidden_size, rnn)
net.load_state_dict(torch.load('relcopylogs/' + args.name + '.pt'))
net.rnn.T = args.T + 20
net.rnn.cutoff = args.cutoff
if CUDA:
net = net.cuda()
net.rnn = net.rnn.cuda()
print('Copy task')
print(NET_TYPE)
print('Cuda: {}'.format(CUDA))
print(nonlin)
print(hidden_size)
