7a6e3b93fb4b97af7b06244b768b1fee4b547c17,ch12/train_crossent.py,,,#,41

Before Change

    net = model.PhraseModel(emb_size=model.EMBEDDING_DIM, dict_size=len(emb_dict),
                            hid_size=model.HIDDEN_STATE_SIZE)
    if args.cuda:
        net.cuda()
    log.info("Model: %s", net)

    writer = SummaryWriter(comment="-" + args.name)

    optimiser = optim.Adam(net.parameters(), lr=LEARNING_RATE)
    best_bleu = None
    for epoch in range(MAX_EPOCHES):
        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,
                                                                   cuda=args.cuda)
            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 = Variable(torch.LongTensor(net_targets))
            if args.cuda:
                targets_v = targets_v.cuda()
            loss_v = F.cross_entropy(results_v, targets_v)
            loss_v.backward()
            optimiser.step()

After Change

    train_data, test_data = data.split_train_test(train_data)
    log.info("Train set has %d phrases, test %d", len(train_data), len(test_data))

    net = model.PhraseModel(emb_size=model.EMBEDDING_DIM, dict_size=len(emb_dict),
                            hid_size=model.HIDDEN_STATE_SIZE).to(device)
    log.info("Model: %s", net)

    writer = SummaryWriter(comment="-" + args.name)

    optimiser = optim.Adam(net.parameters(), lr=LEARNING_RATE)
    best_bleu = None
    for epoch in range(MAX_EPOCHES):
        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()

Instances