def evaluate(data_source, batch_size=10):
model.eval()
if args.model == 'QRNN': model.reset()
total_loss = 0
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(batch_size)
for i in range(0, data_source.size(0) - 1, args.bptt):
data, targets = get_batch(data_source, i, args, evaluation=True)
output, hidden = model(data, hidden)
total_loss += len(data) * criterion(model.decoder.weight, model.decoder.bias, output, targets).data
hidden = repackage_hidden(hidden)
return total_loss.item() / len(data_source)
def train():
if args.model == 'QRNN': model.reset()
total_loss = 0
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(args.batch_size)
batch, i = 0, 0
while i < train_data.size(0) - 1 - 1:
bptt = args.bptt if np.random.random() < 0.95 else args.bptt / 2.
seq_len = max(5, int(np.random.normal(bptt, 5)))
lr2 = optimizer.param_groups[0]['lr']
optimizer.param_groups[0]['lr'] = lr2 * seq_len / args.bptt
model.train()
data, targets = get_batch(train_data, i, args, seq_len=seq_len)
hidden = repackage_hidden(hidden)
optimizer.zero_grad()
output, hidden, rnn_hs, dropped_rnn_hs = model(data,
hidden,
return_h=True)
raw_loss = criterion(model.decoder.weight, model.decoder.bias, output,
targets)
loss = raw_loss
if args.alpha:
loss = loss + sum(args.alpha * dropped_rnn_h.pow(2).mean()
for dropped_rnn_h in dropped_rnn_hs[-1:])
if args.beta:
loss = loss + sum(args.beta *
(rnn_h[1:] - rnn_h[:-1]).pow(2).mean()
for rnn_h in rnn_hs[-1:])
loss.backward()
if args.clip: torch.nn.utils.clip_grad_norm_(params, args.clip)
optimizer.step()
total_loss += raw_loss.data
optimizer.param_groups[0]['lr'] = lr2
if batch % args.log_interval == 0 and batch > 0:
cur_loss = total_loss.item() / args.log_interval
elapsed = time.time() - start_time
print(
'| epoch {:3d} | {:5d}/{:5d} batches | lr {:05.5f} | ms/batch {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f} | bpc {:8.3f}'.format(
epoch, batch,
len(train_data) // args.bptt,
optimizer.param_groups[0]['lr'],
elapsed * 1000 / args.log_interval, cur_loss,
math.exp(cur_loss), cur_loss / math.log(2)))
total_loss = 0
start_time = time.time()
batch += 1
i += seq_len