我的NLP实践之旅05 不好意思，因为自己周五过生日的缘故，鸽了一次的博客，时间一去不复返，经过了周六和周日，转眼这次的NLP组队学习也接近尾声了，于是我乖乖的来补发博客了！
一、Word2Vec
这次的NLP组队学习Task05为深度学习组队时间，分为Word2Vec，TextCNN，和TextRNN三个部分，下面介绍一下Word2Vec

文章图片

简单来说，Word2Vec实际上就是读单词的一次处理，通过神经网络的形式将单词转化为固定的维度，以此来达到增加特征以方便神经网络及其他模型训练的作用。
代码：

import logging import randomimport numpy as np import torchlogging.basicConfig(level=logging.INFO, format='%(asctime)-15s %(levelname)s: %(message)s')# set seed seed = 666 random.seed(seed) np.random.seed(seed) torch.cuda.manual_seed(seed) torch.manual_seed(seed)

logging.info('Start training...') from gensim.models.word2vec import Word2Vecnum_features = 100# Word vector dimensionality num_workers = 8# Number of threads to run in paralleltrain_texts = list(map(lambda x: list(x.split()), train_texts)) model = Word2Vec(train_texts, workers=num_workers, size=num_features) model.init_sims(replace=True)# save model model.save("./word2vec.bin")# load model model = Word2Vec.load("./word2vec.bin")# convert format model.wv.save_word2vec_format('./word2vec.txt', binary=False)

上述代码的作用为将文本中的每一个单词转化为100维的向量。其中，最后两行代码将训练好的Word2Vec模型进行了持久化！
二、TextCNN

文章图片

TextCNN的原理图如上图所示。在我们将单词向量通过Word2Vec的形式Embeding成相同维度后，我们通过自定义的卷积核对其进行卷积操作，以此来得到不同的输出，然后在将卷积层进行池化操作（可以理解为加和），最后对结果进行softMax处理得到分类结果，这就是运用TextCNN进行分类的基本原理。
代码：

import logging import randomimport numpy as np import torchlogging.basicConfig(level=logging.INFO, format='%(asctime)-15s %(levelname)s: %(message)s')# set seed seed = 666 random.seed(seed) np.random.seed(seed) torch.cuda.manual_seed(seed) torch.manual_seed(seed)# set cuda gpu = 0 use_cuda = gpu >= 0 and torch.cuda.is_available() if use_cuda: torch.cuda.set_device(gpu) device = torch.device("cuda", gpu) else: device = torch.device("cpu") logging.info("Use cuda: %s, gpu id: %d.", use_cuda, gpu)

# build model class Model(nn.Module): def __init__(self, vocab): super(Model, self).__init__() self.sent_rep_size = 300 self.doc_rep_size = sent_hidden_size * 2 self.all_parameters = {} parameters = [] self.word_encoder = WordCNNEncoder(vocab) parameters.extend(list(filter(lambda p: p.requires_grad, self.word_encoder.parameters())))self.sent_encoder = SentEncoder(self.sent_rep_size) self.sent_attention = Attention(self.doc_rep_size) parameters.extend(list(filter(lambda p: p.requires_grad, self.sent_encoder.parameters()))) parameters.extend(list(filter(lambda p: p.requires_grad, self.sent_attention.parameters())))self.out = nn.Linear(self.doc_rep_size, vocab.label_size, bias=True) parameters.extend(list(filter(lambda p: p.requires_grad, self.out.parameters())))if use_cuda: self.to(device)if len(parameters) > 0: self.all_parameters["basic_parameters"] = parameterslogging.info('Build model with cnn word encoder, lstm sent encoder.')para_num = sum([np.prod(list(p.size())) for p in self.parameters()]) logging.info('Model param num: %.2f M.' % (para_num / 1e6))def forward(self, batch_inputs): # batch_inputs(batch_inputs1, batch_inputs2): b x doc_len x sent_len # batch_masks : b x doc_len x sent_len batch_inputs1, batch_inputs2, batch_masks = batch_inputs batch_size, max_doc_len, max_sent_len = batch_inputs1.shape[0], batch_inputs1.shape[1], batch_inputs1.shape[2] batch_inputs1 = batch_inputs1.view(batch_size * max_doc_len, max_sent_len)# sen_num x sent_len batch_inputs2 = batch_inputs2.view(batch_size * max_doc_len, max_sent_len)# sen_num x sent_len batch_masks = batch_masks.view(batch_size * max_doc_len, max_sent_len)# sen_num x sent_lensent_reps = self.word_encoder(batch_inputs1, batch_inputs2)# sen_num x sent_rep_sizesent_reps = sent_reps.view(batch_size, max_doc_len, self.sent_rep_size)# b x doc_len x sent_rep_size batch_masks = batch_masks.view(batch_size, max_doc_len, max_sent_len)# b x doc_len x max_sent_len sent_masks = batch_masks.bool().any(2).float()# b x doc_lensent_hiddens = self.sent_encoder(sent_reps, sent_masks)# b x doc_len x doc_rep_size doc_reps, atten_scores = self.sent_attention(sent_hiddens, sent_masks)# b x doc_rep_sizebatch_outputs = self.out(doc_reps)# b x num_labelsreturn batch_outputsmodel = Model(vocab)

# build optimizer learning_rate = 2e-4 decay = .75 decay_step = 1000class Optimizer: def __init__(self, model_parameters): self.all_params = [] self.optims = [] self.schedulers = []for name, parameters in model_parameters.items(): if name.startswith("basic"): optim = torch.optim.Adam(parameters, lr=learning_rate) self.optims.append(optim)l = lambda step: decay ** (step // decay_step) scheduler = torch.optim.lr_scheduler.LambdaLR(optim, lr_lambda=l) self.schedulers.append(scheduler) self.all_params.extend(parameters)else: Exception("no nameed parameters.")self.num = len(self.optims)def step(self): for optim, scheduler in zip(self.optims, self.schedulers): optim.step() scheduler.step() optim.zero_grad()def zero_grad(self): for optim in self.optims: optim.zero_grad()def get_lr(self): lrs = tuple(map(lambda x: x.get_lr()[-1], self.schedulers)) lr = ' %.5f' * self.num res = lr % lrs return res

# build trainerimport time from sklearn.metrics import classification_reportclip = 5.0 epochs = 1 early_stops = 3 log_interval = 50test_batch_size = 128 train_batch_size = 128save_model = './cnn.bin' save_test = './cnn.csv'class Trainer(): def __init__(self, model, vocab): self.model = model self.report = Trueself.train_data = https://www.it610.com/article/get_examples(train_data, vocab) self.batch_num = int(np.ceil(len(self.train_data) / float(train_batch_size))) self.dev_data = get_examples(dev_data, vocab)# criterion self.criterion = nn.CrossEntropyLoss()# label name self.target_names = vocab.target_names# optimizer self.optimizer = Optimizer(model.all_parameters)# count self.step = 0 self.early_stop = -1 self.best_train_f1, self.best_dev_f1 = 0, 0 self.last_epoch = epochsdef train(self): logging.info('Start training...') for epoch in range(1, epochs + 1): train_f1 = self._train(epoch)dev_f1 = self._eval(epoch)if self.best_dev_f1 <= dev_f1: logging.info( "Exceed history dev = %.2f, current dev = %.2f" % (self.best_dev_f1, dev_f1)) torch.save(self.model.state_dict(), save_model)self.best_train_f1 = train_f1 self.best_dev_f1 = dev_f1 self.early_stop = 0 else: self.early_stop += 1 if self.early_stop == early_stops: logging.info( "Eearly stop in epoch %d, best train: %.2f, dev: %.2f" % ( epoch - early_stops, self.best_train_f1, self.best_dev_f1)) self.last_epoch = epoch breakdef test(self): self.model.load_state_dict(torch.load(save_model)) self._eval(self.last_epoch + 1, test=True)def _train(self, epoch): self.optimizer.zero_grad() self.model.train()start_time = time.time() epoch_start_time = time.time() overall_losses = 0 losses = 0 batch_idx = 1 y_pred = [] y_true = [] for batch_data in data_iter(self.train_data, train_batch_size, shuffle=True): torch.cuda.empty_cache() batch_inputs, batch_labels = self.batch2tensor(batch_data) batch_outputs = self.model(batch_inputs) loss = self.criterion(batch_outputs, batch_labels) loss.backward()loss_value = https://www.it610.com/article/loss.detach().cpu().item() losses += loss_value overall_losses += loss_valuey_pred.extend(torch.max(batch_outputs, dim=1)[1].cpu().numpy().tolist()) y_true.extend(batch_labels.cpu().numpy().tolist())nn.utils.clip_grad_norm_(self.optimizer.all_params, max_norm=clip) for optimizer, scheduler in zip(self.optimizer.optims, self.optimizer.schedulers): optimizer.step() scheduler.step() self.optimizer.zero_grad()self.step += 1if batch_idx % log_interval == 0: elapsed = time.time() - start_timelrs = self.optimizer.get_lr() logging.info('| epoch {:3d} | step {:3d} | batch {:3d}/{:3d} | lr{} | loss {:.4f} | s/batch {:.2f}'.format( epoch, self.step, batch_idx, self.batch_num, lrs, losses / log_interval, elapsed / log_interval))losses = 0 start_time = time.time()batch_idx += 1overall_losses /= self.batch_num during_time = time.time() - epoch_start_time# reformat overall_losses = reformat(overall_losses, 4) score, f1 = get_score(y_true, y_pred)logging.info( '| epoch {:3d} | score {} | f1 {} | loss {:.4f} | time {:.2f}'.format(epoch, score, f1, overall_losses, during_time)) if set(y_true) == set(y_pred) and self.report: report = classification_report(y_true, y_pred, digits=4, target_names=self.target_names) logging.info('\n' + report)return f1def _eval(self, epoch, test=False): self.model.eval() start_time = time.time() data = https://www.it610.com/article/self.test_data if test else self.dev_data y_pred = [] y_true = [] with torch.no_grad(): for batch_data in data_iter(data, test_batch_size, shuffle=False): torch.cuda.empty_cache() batch_inputs, batch_labels = self.batch2tensor(batch_data) batch_outputs = self.model(batch_inputs) y_pred.extend(torch.max(batch_outputs, dim=1)[1].cpu().numpy().tolist()) y_true.extend(batch_labels.cpu().numpy().tolist())score, f1 = get_score(y_true, y_pred)during_time = time.time() - start_timeif test: df = pd.DataFrame({'label': y_pred}) df.to_csv(save_test, index=False, sep=',') else: logging.info( '| epoch {:3d} | dev | score {} | f1 {} | time {:.2f}'.format(epoch, score, f1, during_time)) if set(y_true) == set(y_pred) and self.report: report = classification_report(y_true, y_pred, digits=4, target_names=self.target_names) logging.info('\n' + report)return f1def batch2tensor(self, batch_data): ''' [[label, doc_len, [[sent_len, [sent_id0, ...], [sent_id1, ...]], ...]] ''' batch_size = len(batch_data) doc_labels = [] doc_lens = [] doc_max_sent_len = [] for doc_data in batch_data: doc_labels.append(doc_data[0]) doc_lens.append(doc_data[1]) sent_lens = [sent_data[0] for sent_data in doc_data[2]] max_sent_len = max(sent_lens) doc_max_sent_len.append(max_sent_len)max_doc_len = max(doc_lens) max_sent_len = max(doc_max_sent_len)batch_inputs1 = torch.zeros((batch_size, max_doc_len, max_sent_len), dtype=torch.int64) batch_inputs2 = torch.zeros((batch_size, max_doc_len, max_sent_len), dtype=torch.int64) batch_masks = torch.zeros((batch_size, max_doc_len, max_sent_len), dtype=torch.float32) batch_labels = torch.LongTensor(doc_labels)for b in range(batch_size): for sent_idx in range(doc_lens[b]): sent_data = https://www.it610.com/article/batch_data[b][2][sent_idx] for word_idx in range(sent_data[0]): batch_inputs1[b, sent_idx, word_idx] = sent_data[1][word_idx] batch_inputs2[b, sent_idx, word_idx] = sent_data[2][word_idx] batch_masks[b, sent_idx, word_idx] = 1if use_cuda: batch_inputs1 = batch_inputs1.to(device) batch_inputs2 = batch_inputs2.to(device) batch_masks = batch_masks.to(device) batch_labels = batch_labels.to(device)return (batch_inputs1, batch_inputs2, batch_masks), batch_labels# train trainer = Trainer(model, vocab) trainer.train()

三、TextRNN
【我的NLP实践之旅05】TextRNN与TextCNN的主要区别为在神经网络中间层采取了LSTM的形式，区别代码如下：

# build model class Model(nn.Module): def __init__(self, vocab): super(Model, self).__init__() self.sent_rep_size = word_hidden_size * 2 self.doc_rep_size = sent_hidden_size * 2 self.all_parameters = {} parameters = [] self.word_encoder = WordLSTMEncoder(vocab) self.word_attention = Attention(self.sent_rep_size) parameters.extend(list(filter(lambda p: p.requires_grad, self.word_encoder.parameters()))) parameters.extend(list(filter(lambda p: p.requires_grad, self.word_attention.parameters())))self.sent_encoder = SentEncoder(self.sent_rep_size) self.sent_attention = Attention(self.doc_rep_size) parameters.extend(list(filter(lambda p: p.requires_grad, self.sent_encoder.parameters()))) parameters.extend(list(filter(lambda p: p.requires_grad, self.sent_attention.parameters())))self.out = nn.Linear(self.doc_rep_size, vocab.label_size, bias=True) parameters.extend(list(filter(lambda p: p.requires_grad, self.out.parameters())))if use_cuda: self.to(device)if len(parameters) > 0: self.all_parameters["basic_parameters"] = parameterslogging.info('Build model with lstm word encoder, lstm sent encoder.')para_num = sum([np.prod(list(p.size())) for p in self.parameters()]) logging.info('Model param num: %.2f M.' % (para_num / 1e6))def forward(self, batch_inputs): # batch_inputs(batch_inputs1, batch_inputs2): b x doc_len x sent_len # batch_masks : b x doc_len x sent_len batch_inputs1, batch_inputs2, batch_masks = batch_inputs batch_size, max_doc_len, max_sent_len = batch_inputs1.shape[0], batch_inputs1.shape[1], batch_inputs1.shape[2] batch_inputs1 = batch_inputs1.view(batch_size * max_doc_len, max_sent_len)# sen_num x sent_len batch_inputs2 = batch_inputs2.view(batch_size * max_doc_len, max_sent_len)# sen_num x sent_len batch_masks = batch_masks.view(batch_size * max_doc_len, max_sent_len)# sen_num x sent_lenbatch_hiddens = self.word_encoder(batch_inputs1, batch_inputs2, batch_masks)# sen_num x sent_len x sent_rep_size sent_reps, atten_scores = self.word_attention(batch_hiddens, batch_masks)# sen_num x sent_rep_sizesent_reps = sent_reps.view(batch_size, max_doc_len, self.sent_rep_size)# b x doc_len x sent_rep_size batch_masks = batch_masks.view(batch_size, max_doc_len, max_sent_len)# b x doc_len x max_sent_len sent_masks = batch_masks.bool().any(2).float()# b x doc_lensent_hiddens = self.sent_encoder(sent_reps, sent_masks)# b x doc_len x doc_rep_size doc_reps, atten_scores = self.sent_attention(sent_hiddens, sent_masks)# b x doc_rep_sizebatch_outputs = self.out(doc_reps)# b x num_labelsreturn batch_outputsmodel = Model(vocab)

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