[ 2.1937, -0.5535, -0.9000, ..., -0.1032, 0.3514, -1.2759],
[-0.8078, 0.1575, 1.1064, ..., 0.1365, 0.4121, -0.4211]],
device='cuda:0')'''
input_seq, out_seq_list, _, out_idx = model.pack_batch(
batch, net.emb, device)
enc = net.encode(input_seq)
net_results = []
net_targets = []
for idx, out_seq in enumerate(out_seq_list):
ref_indices = out_idx[idx][1:]
enc_item = net.get_encoded_item(enc, idx)
# teacher forcing做训练；
if random.random() < TEACHER_PROB:
r = net.decode_teacher(enc_item, out_seq)
blue_temp = model.seq_bleu(r, ref_indices)
bleu_sum += blue_temp
# Get predicted tokens.
seq = torch.max(r.data, dim=1)[1]
seq = seq.cpu().numpy()
# argmax做训练；
else:
r, seq = net.decode_chain_argmax(enc_item,
out_seq.data[0:1],
len(ref_indices))
blue_temp = utils.calc_bleu(seq, ref_indices)
bleu_sum += blue_temp
net_results.append(r)
net_targets.extend(ref_indices)
bleu_count += 1
losses = []
bleu_sum = 0.0
bleu_count = 0
for batch in data.iterate_batches(train_data, BATCH_SIZE):
optimiser.zero_grad()
input_seq, out_seq_list, _, out_idx = model.pack_batch(batch, net.emb, device)
enc = net.encode(input_seq)
net_results = []
net_targets = []
for idx, out_seq in enumerate(out_seq_list):
ref_indices = out_idx[idx][1:]
enc_item = net.get_encoded_item(enc, idx)
if random.random() < TEACHER_PROB:
r = net.decode_teacher(enc_item, out_seq)
bleu_sum += model.seq_bleu(r, ref_indices)
else:
r, seq = net.decode_chain_argmax(enc_item, out_seq.data[0:1],
len(ref_indices))
bleu_sum += utils.calc_bleu(seq, ref_indices)
net_results.append(r)
net_targets.extend(ref_indices)
bleu_count += 1
results_v = torch.cat(net_results)
targets_v = torch.LongTensor(net_targets).to(device)
loss_v = F.cross_entropy(results_v, targets_v)
loss_v.backward()
optimiser.step()
losses.append(loss_v.item())
bleu = bleu_sum / bleu_count